{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 划分网格然后把变量定义在np数组里"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymesh\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "# 创建一个四面体圆柱网格\n",
    "vertices = np.array([\n",
    "    [0, 0, 0],\n",
    "    [1, 0, 0],\n",
    "    [0, 1, 0],\n",
    "    [0, 0, 1],\n",
    "    [0, 0, 2],\n",
    "    [1, 0, 2],\n",
    "    [0, 1, 2],\n",
    "    [0, 0, 3]\n",
    "])\n",
    "faces = np.array([\n",
    "    [0, 1, 2],\n",
    "    [0, 1, 3],\n",
    "    [0, 2, 3],\n",
    "    [1, 2, 3],\n",
    "    [4, 5, 6],\n",
    "    [4, 5, 7],\n",
    "    [4, 6, 7],\n",
    "    [5, 6, 7],\n",
    "    [0, 1, 4],\n",
    "    [1, 4, 5],\n",
    "    [1, 2, 5],\n",
    "    [2, 5, 6],\n",
    "    [2, 3, 6],\n",
    "    [3, 6, 7],\n",
    "    [3, 0, 7],\n",
    "    [0, 4, 7]\n",
    "])\n",
    "\n",
    "mesh = pymesh.form_mesh(vertices, faces)\n",
    "\n",
    "# 可视化四面体圆柱网格\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.plot_trisurf(mesh.vertices[:, 0], mesh.vertices[:, 1], mesh.vertices[:, 2], triangles=mesh.faces)\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Y')\n",
    "ax.set_zlabel('Z')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "# 两流体模型参数\n",
    "# 假设你已经有了一些基本的物理参数\n",
    "rho_l = 1000  # 液相密度 kg/m^3\n",
    "rho_g = 1  # 气相密度 kg/m^3\n",
    "mu_l = 0.001  # 液相粘度 Pa.s\n",
    "mu_g = 1e-5  # 气相粘度 Pa.s\n",
    "g = 9.81  # 重力加速度 m/s^2\n",
    "\n",
    "# 初始化网格上的物理量\n",
    "mesh.add_attribute(\"face_centroid\")\n",
    "mesh.add_attribute(\"face_area\")\n",
    "mesh.add_attribute(\"face_normal\")\n",
    "\n",
    "# 计算每个面的中心和面积\n",
    "face_centroids = mesh.get_face_attribute(\"face_centroid\")\n",
    "face_areas = mesh.get_face_attribute(\"face_area\")\n",
    "face_normals = mesh.get_face_attribute(\"face_normal\")\n",
    "\n",
    "# 初始化两流体模型的变量\n",
    "alpha_l = np.zeros(mesh.num_faces)  # 液相体积分数\n",
    "alpha_g = np.zeros(mesh.num_faces)  # 气相体积分数\n",
    "velocity_l = np.zeros(mesh.num_faces)  # 液相速度\n",
    "velocity_g = np.zeros(mesh.num_faces)  # 气相速度\n",
    "pressure = np.zeros(mesh.num_faces)  # 压力\n",
    "enthalpy_l = np.zeros(mesh.num_faces)  # 液相焓\n",
    "enthalpy_g = np.zeros(mesh.num_faces)  # 气相焓\n",
    "\n",
    "# 假设初始条件\n",
    "alpha_l[:] = 0.5\n",
    "alpha_g[:] = 0.5\n",
    "velocity_l[:] = 1.0  # 假设初始液相速度为1 m/s\n",
    "velocity_g[:] = 0.1  # 假设初始气相速度为0.1 m/s\n",
    "pressure[:] = 1e5  # 假设初始压力为1 bar\n",
    "enthalpy_l[:] = 1000  # 假设初始液相焓为1000 J/kg\n",
    "enthalpy_g[:] = 2000  # 假设初始气相焓为2000 J/kg\n",
    "\n",
    "# 定义控制容积方程\n",
    "def mass_conservation(alpha_l, alpha_g, rho_l, rho_g):\n",
    "    \"\"\"\n",
    "    质量守恒方程\n",
    "    :param alpha_l: 液相体积分数\n",
    "    :param alpha_g: 气相体积分数\n",
    "    :param rho_l: 液相密度\n",
    "    :param rho_g: 气相密度\n",
    "    :return: 质量守恒结果\n",
    "    \"\"\"\n",
    "    return alpha_l * rho_l + alpha_g * rho_g\n",
    "\n",
    "def axial_momentum_conservation(alpha_l, alpha_g, rho_l, rho_g, velocity_l, velocity_g, pressure, g):\n",
    "    \"\"\"\n",
    "    轴向动量守恒方程\n",
    "    :param alpha_l: 液相体积分数\n",
    "    :param alpha_g: 气相体积分数\n",
    "    :param rho_l: 液相密度\n",
    "    :param rho_g: 气相密度\n",
    "    :param velocity_l: 液相速度\n",
    "    :param velocity_g: 气相速度\n",
    "    :param pressure: 压力\n",
    "    :param g: 重力加速度\n",
    "    :return: 轴向动量守恒结果\n",
    "    \"\"\"\n",
    "    return alpha_l * rho_l * velocity_l + alpha_g * rho_g * velocity_g + pressure - rho_l * g\n",
    "\n",
    "def lateral_momentum_conservation(alpha_l, alpha_g, rho_l, rho_g, velocity_l, velocity_g, pressure):\n",
    "    \"\"\"\n",
    "    横向动量守恒方程\n",
    "    :param alpha_l: 液相体积分数\n",
    "    :param alpha_g: 气相体积分数\n",
    "    :param rho_l: 液相密度\n",
    "    :param rho_g: 气相密度\n",
    "    :param velocity_l: 液相速度\n",
    "    :param velocity_g: 气相速度\n",
    "    :param pressure: 压力\n",
    "    :return: 横向动量守恒结果\n",
    "    \"\"\"\n",
    "    return alpha_l * rho_l * velocity_l**2 + alpha_g * rho_g * velocity_g**2 + pressure\n",
    "\n",
    "def liquid_enthalpy_conservation(alpha_l, enthalpy_l, velocity_l):\n",
    "    \"\"\"\n",
    "    液相焓守恒方程\n",
    "    :param alpha_l: 液相体积分数\n",
    "    :param enthalpy_l: 液相焓\n",
    "    :param velocity_l: 液相速度\n",
    "    :return: 液相焓守恒结果\n",
    "    \"\"\"\n",
    "    return alpha_l * enthalpy_l * velocity_l\n",
    "\n",
    "def gas_enthalpy_conservation(alpha_g, enthalpy_g, velocity_g):\n",
    "    \"\"\"\n",
    "    气相焓守恒方程\n",
    "    :param alpha_g: 气相体积分数\n",
    "    :param enthalpy_g: 气相焓\n",
    "    :param velocity_g: 气相速度\n",
    "    :return: 气相焓守恒结果\n",
    "    \"\"\"\n",
    "    return alpha_g * enthalpy_g * velocity_g\n",
    "\n",
    "def mixture_enthalpy_conservation(alpha_l, alpha_g, enthalpy_l, enthalpy_g, velocity_l, velocity_g):\n",
    "    \"\"\"\n",
    "    混合物焓守恒方程\n",
    "    :param alpha_l: 液相体积分数\n",
    "    :param alpha_g: 气相体积分数\n",
    "    :param enthalpy_l: 液相焓\n",
    "    :param enthalpy_g: 气相焓\n",
    "    :param velocity_l: 液相速度\n",
    "    :param velocity_g: 气相速度\n",
    "    :return: 混合物焓守恒结果\n",
    "    \"\"\"\n",
    "    return alpha_l * enthalpy_l * velocity_l + alpha_g * enthalpy_g * velocity_g\n",
    "\n",
    "# 计算控制容积内的守恒量\n",
    "for i in range(mesh.num_faces):\n",
    "    mass = mass_conservation(alpha_l[i], alpha_g[i], rho_l, rho_g)\n",
    "    axial_momentum = axial_momentum_conservation(alpha_l[i], alpha_g[i], rho_l, rho_g, velocity_l[i], velocity_g[i], pressure[i], g)\n",
    "    lateral_momentum = lateral_momentum_conservation(alpha_l[i], alpha_g[i], rho_l, rho_g, velocity_l[i], velocity_g[i], pressure[i])\n",
    "    liquid_enthalpy = liquid_enthalpy_conservation(alpha_l[i], enthalpy_l[i], velocity_l[i])\n",
    "    gas_enthalpy = gas_enthalpy_conservation(alpha_g[i], enthalpy_g[i], velocity_g[i])\n",
    "    mixture_enthalpy = mixture_enthalpy_conservation(alpha_l[i], alpha_g[i], enthalpy_l[i], enthalpy_g[i], velocity_l[i], velocity_g[i])\n",
    "\n",
    "    print(f\"Face {i}:\")\n",
    "    print(f\"Mass: {mass}\")\n",
    "    print(f\"Axial Momentum: {axial_momentum}\")\n",
    "    print(f\"Lateral Momentum: {lateral_momentum}\")\n",
    "    print(f\"Liquid Enthalpy: {liquid_enthalpy}\")\n",
    "    print(f\"Gas Enthalpy: {gas_enthalpy}\")\n",
    "    print(f\"Mixture Enthalpy: {mixture_enthalpy}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 划分网格然后把物理量定义在pgmesh网格里"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymesh\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "# 创建一个四面体圆柱网格\n",
    "vertices = np.array([\n",
    "    [0, 0, 0],\n",
    "    [1, 0, 0],\n",
    "    [0, 1, 0],\n",
    "    [0, 0, 1],\n",
    "    [0, 0, 2],\n",
    "    [1, 0, 2],\n",
    "    [0, 1, 2],\n",
    "    [0, 0, 3]\n",
    "])\n",
    "faces = np.array([\n",
    "    [0, 1, 2],\n",
    "    [0, 1, 3],\n",
    "    [0, 2, 3],\n",
    "    [1, 2, 3],\n",
    "    [4, 5, 6],\n",
    "    [4, 5, 7],\n",
    "    [4, 6, 7],\n",
    "    [5, 6, 7],\n",
    "    [0, 1, 4],\n",
    "    [1, 4, 5],\n",
    "    [1, 2, 5],\n",
    "    [2, 5, 6],\n",
    "    [2, 3, 6],\n",
    "    [3, 6, 7],\n",
    "    [3, 0, 7],\n",
    "    [0, 4, 7]\n",
    "])\n",
    "\n",
    "mesh = pymesh.form_mesh(vertices, faces)\n",
    "\n",
    "# 可视化四面体圆柱网格\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.plot_trisurf(mesh.vertices[:, 0], mesh.vertices[:, 1], mesh.vertices[:, 2], triangles=mesh.faces)\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Y')\n",
    "ax.set_zlabel('Z')\n",
    "plt.show()\n",
    "\n",
    "# 输出网格的连接信息\n",
    "for face in mesh.faces:\n",
    "    print(f\"Face: {face}\")\n",
    "\n",
    "# 两流体模型参数\n",
    "# 假设你已经有了一些基本的物理参数\n",
    "rho_l = 1000  # 液相密度 kg/m^3\n",
    "rho_g = 1  # 气相密度 kg/m^3\n",
    "mu_l = 0.001  # 液相粘度 Pa.s\n",
    "mu_g = 1e-5  # 气相粘度 Pa.s\n",
    "g = 9.81  # 重力加速度 m/s^2\n",
    "\n",
    "# 初始化网格上的物理量\n",
    "mesh.add_attribute(\"face_centroid\")\n",
    "mesh.add_attribute(\"face_area\")\n",
    "mesh.add_attribute(\"face_normal\")\n",
    "\n",
    "# 计算每个面的中心和面积\n",
    "face_centroids = mesh.get_face_attribute(\"face_centroid\")\n",
    "face_areas = mesh.get_face_attribute(\"face_area\")\n",
    "face_normals = mesh.get_face_attribute(\"face_normal\")\n",
    "\n",
    "# 初始化两流体模型的变量\n",
    "mesh.add_attribute(\"alpha_l\")\n",
    "mesh.add_attribute(\"alpha_g\")\n",
    "mesh.add_attribute(\"velocity_l\")\n",
    "mesh.add_attribute(\"velocity_g\")\n",
    "mesh.add_attribute(\"pressure\")\n",
    "mesh.add_attribute(\"enthalpy_l\")\n",
    "mesh.add_attribute(\"enthalpy_g\")\n",
    "\n",
    "# 假设初始条件\n",
    "mesh.set_attribute(\"alpha_l\", np.full(mesh.num_faces, 0.5))\n",
    "mesh.set_attribute(\"alpha_g\", np.full(mesh.num_faces, 0.5))\n",
    "mesh.set_attribute(\"velocity_l\", np.full(mesh.num_faces, 1.0))\n",
    "mesh.set_attribute(\"velocity_g\", np.full(mesh.num_faces, 0.1))\n",
    "mesh.set_attribute(\"pressure\", np.full(mesh.num_faces, 1e5))\n",
    "mesh.set_attribute(\"enthalpy_l\", np.full(mesh.num_faces, 1000))\n",
    "mesh.set_attribute(\"enthalpy_g\", np.full(mesh.num_faces, 2000))\n",
    "\n",
    "# 定义控制容积方程\n",
    "def mass_conservation(alpha_l, alpha_g, rho_l, rho_g):\n",
    "    \"\"\"\n",
    "    质量守恒方程\n",
    "    :param alpha_l: 液相体积分数\n",
    "    :param alpha_g: 气相体积分数\n",
    "    :param rho_l: 液相密度\n",
    "    :param rho_g: 气相密度\n",
    "    :return: 质量守恒结果\n",
    "    \"\"\"\n",
    "    return alpha_l * rho_l + alpha_g * rho_g\n",
    "\n",
    "def axial_momentum_conservation(alpha_l, alpha_g, rho_l, rho_g, velocity_l, velocity_g, pressure, g):\n",
    "    \"\"\"\n",
    "    轴向动量守恒方程\n",
    "    :param alpha_l: 液相体积分数\n",
    "    :param alpha_g: 气相体积分数\n",
    "    :param rho_l: 液相密度\n",
    "    :param rho_g: 气相密度\n",
    "    :param velocity_l: 液相速度\n",
    "    :param velocity_g: 气相速度\n",
    "    :param pressure: 压力\n",
    "    :param g: 重力加速度\n",
    "    :return: 轴向动量守恒结果\n",
    "    \"\"\"\n",
    "    return alpha_l * rho_l * velocity_l + alpha_g * rho_g * velocity_g + pressure - rho_l * g\n",
    "\n",
    "def lateral_momentum_conservation(alpha_l, alpha_g, rho_l, rho_g, velocity_l, velocity_g, pressure):\n",
    "    \"\"\"\n",
    "    横向动量守恒方程\n",
    "    :param alpha_l: 液相体积分数\n",
    "    :param alpha_g: 气相体积分数\n",
    "    :param rho_l: 液相密度\n",
    "    :param rho_g: 气相密度\n",
    "    :param velocity_l: 液相速度\n",
    "    :param velocity_g: 气相速度\n",
    "    :param pressure: 压力\n",
    "    :return: 横向动量守恒结果\n",
    "    \"\"\"\n",
    "    return alpha_l * rho_l * velocity_l**2 + alpha_g * rho_g * velocity_g**2 + pressure\n",
    "\n",
    "def liquid_enthalpy_conservation(alpha_l, enthalpy_l, velocity_l):\n",
    "    \"\"\"\n",
    "    液相焓守恒方程\n",
    "    :param alpha_l: 液相体积分数\n",
    "    :param enthalpy_l: 液相焓\n",
    "    :param velocity_l: 液相速度\n",
    "    :return: 液相焓守恒结果\n",
    "    \"\"\"\n",
    "    return alpha_l * enthalpy_l * velocity_l\n",
    "\n",
    "def gas_enthalpy_conservation(alpha_g, enthalpy_g, velocity_g):\n",
    "    \"\"\"\n",
    "    气相焓守恒方程\n",
    "    :param alpha_g: 气相体积分数\n",
    "    :param enthalpy_g: 气相焓\n",
    "    :param velocity_g: 气相速度\n",
    "    :return: 气相焓守恒结果\n",
    "    \"\"\"\n",
    "    return alpha_g * enthalpy_g * velocity_g\n",
    "\n",
    "def mixture_enthalpy_conservation(alpha_l, alpha_g, enthalpy_l, enthalpy_g, velocity_l, velocity_g):\n",
    "    \"\"\"\n",
    "    混合物焓守恒方程\n",
    "    :param alpha_l: 液相体积分数\n",
    "    :param alpha_g: 气相体积分数\n",
    "    :param enthalpy_l: 液相焓\n",
    "    :param enthalpy_g: 气相焓\n",
    "    :param velocity_l: 液相速度\n",
    "    :param velocity_g: 气相速度\n",
    "    :return: 混合物焓守恒结果\n",
    "    \"\"\"\n",
    "    return alpha_l * enthalpy_l * velocity_l + alpha_g * enthalpy_g * velocity_g\n",
    "\n",
    "\n",
    "# 计算控制容积内的守恒量\n",
    "for i in range(mesh.num_faces):\n",
    "    alpha_l = mesh.get_attribute(\"alpha_l\")[i]\n",
    "    alpha_g = mesh.get_attribute(\"alpha_g\")[i]\n",
    "    velocity_l = mesh.get_attribute(\"velocity_l\")[i]\n",
    "    velocity_g = mesh.get_attribute(\"velocity_g\")[i]\n",
    "    pressure = mesh.get_attribute(\"pressure\")[i]\n",
    "    enthalpy_l = mesh.get_attribute(\"enthalpy_l\")[i]\n",
    "    enthalpy_g = mesh.get_attribute(\"enthalpy_g\")[i]\n",
    "\n",
    "    mass = mass_conservation(alpha_l, alpha_g, rho_l, rho_g)\n",
    "    axial_momentum = axial_momentum_conservation(alpha_l, alpha_g, rho_l, rho_g, velocity_l, velocity_g, pressure, g)\n",
    "    lateral_momentum = lateral_momentum_conservation(alpha_l, alpha_g, rho_l, rho_g, velocity_l, velocity_g, pressure)\n",
    "    liquid_enthalpy = liquid_enthalpy_conservation(alpha_l, enthalpy_l, velocity_l)\n",
    "    gas_enthalpy = gas_enthalpy_conservation(alpha_g, enthalpy_g, velocity_g)\n",
    "    mixture_enthalpy = mixture_enthalpy_conservation(alpha_l, alpha_g, enthalpy_l, enthalpy_g, velocity_l, velocity_g)\n",
    "\n",
    "    print(f\"Face {i}:\")\n",
    "    print(f\"Mass: {mass}\")\n",
    "    print(f\"Axial Momentum: {axial_momentum}\")\n",
    "    print(f\"Lateral Momentum: {lateral_momentum}\")\n",
    "    print(f\"Liquid Enthalpy: {liquid_enthalpy}\")\n",
    "    print(f\"Gas Enthalpy: {gas_enthalpy}\")\n",
    "    print(f\"Mixture Enthalpy: {mixture_enthalpy}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 19977 (\\N{CJK UNIFIED IDEOGRAPH-4E09}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 38454 (\\N{CJK UNIFIED IDEOGRAPH-9636}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 38663 (\\N{CJK UNIFIED IDEOGRAPH-9707}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 21160 (\\N{CJK UNIFIED IDEOGRAPH-52A8}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 26041 (\\N{CJK UNIFIED IDEOGRAPH-65B9}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 31243 (\\N{CJK UNIFIED IDEOGRAPH-7A0B}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 30340 (\\N{CJK UNIFIED IDEOGRAPH-7684}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 21487 (\\N{CJK UNIFIED IDEOGRAPH-53EF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 35270 (\\N{CJK UNIFIED IDEOGRAPH-89C6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 21270 (\\N{CJK UNIFIED IDEOGRAPH-5316}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 20998 (\\N{CJK UNIFIED IDEOGRAPH-5206}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 26512 (\\N{CJK UNIFIED IDEOGRAPH-6790}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 38388 (\\N{CJK UNIFIED IDEOGRAPH-95F4}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 25391 (\\N{CJK UNIFIED IDEOGRAPH-632F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 24133 (\\N{CJK UNIFIED IDEOGRAPH-5E45}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2IAAAIhCAYAAAAsFAnkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3gc1bn48e9sUe/dKrZlW25y7wVjU2yCEwhgSgIXQgIkpNFubm5IuyG5CT/uTTEhNx1sCDWEAAFMMcXYgHHvlrvVe+/Slvn9MTsryZZtld2d3dH7eR49llazM0fj3Z15z3nPexRVVVWEEEIIIYQQQgSMxegGCCGEEEIIIcRII4GYEEIIIYQQQgSYBGJCCCGEEEIIEWASiAkhhBBCCCFEgEkgJoQQQgghhBABJoGYEEIIIYQQQgSYBGJCCCGEEEIIEWASiAkhhBBCCCFEgEkgJoQQQgghhBABZjO6AUIIIcytoKCAxYsXn3ebLVu2sGzZsvNus2PHDpxO5wX3JdsFbru8vLzzbiOEEOLcJBATQgjhVy6Xi2nTpvHRRx/1+/uLLroIRVEuuI3L5RrQvmS7wG0nhBBi6CQ1UQghhBBCCCECTAIxIYQQQgghhAgwCcSEEEIIIYQQIsAkEBNCCCGEEEKIAJNATAghhBBCCCECTAIxIYQQQgghhAgwCcSEEEIIIYQQIsAkEBNCCCGEEEKIAJNATAghhBBCCCECTAIxIYQQQgghhAgwCcSEEEIIIYQQIsAkEBNCCCGEEEKIALMZ3QAhhBDmZrVa2bdvHwkJCf3+3uVy4XK5LriNxWIZ0L5ku8BtJ4QQYugUVVVVoxshhBBCCCGEECOJdGcJIYQQQgghRIBJICaEEEIIIYQQASaBmBBCCCGEEEIEmBTr8AG32015eTmxsbEoimJ0c4QQQgghhBAGUVWVlpYWMjMzz1vYSAIxHygvLycnJ8foZgghhBBCCCGCRElJCdnZ2ef8vQRiPhAbGwtoJzsuLs7QtjgcDt555x1WrVqF3W43tC1mJOfXv+T8+pecX/+S8+tfcn79T86xf8n59a9gOr/Nzc3k5OR4Y4RzkUDMB/R0xLi4uKAIxKKiooiLizP8RWhGcn79S86vf8n59S85v/4l59f/5Bz7l5xf/wrG83uhKUtSrEMIIYQQQgghAkwCMSGEEEIIIYQIMAnEhBBCCCGEECLAZI6YEEIIIYQQI4zL5cLhcBjdDJ9xOBzYbDY6OztxuVx+PZbVasVmsw172SoJxIQQQgghhBhBWltbKS0tRVVVo5viM6qqkpGRQUlJSUDW9Y2KimLUqFGEhYUNeR8SiAkhhBBCCDFCuFwuSktLiYqKIjU1NSBBSyC43W5aW1uJiYk57yLKw6WqKt3d3dTU1HD69Gny8vKGfDwJxIQQQgghhBghHA4HqqqSmppKZGSk0c3xGbfbTXd3NxEREX4NxAAiIyOx2+0UFRV5jzkUUqxDCCGEEEKIEcYsI2FG8UWwJ4GYEEIIIYQQQgSYBGJCCCGEEEIIEWASiAkhhBBCCCFC3urVq3n22WfPu83vfvc7rr766gC16PwkEBNCCCGEEEKEtNdff53q6mq+8IUveB9TFIVXXnmlz3Z33XUXO3bs4KOPPgpwC88mgZgQQgghhBAipD322GPcfPPNFyyiER4ezs0338xjjz0WoJadmwRiQgghhBBCjFCqqtLe7TTka6ALSj/11FMkJyfT1dXV5/E1a9Zw2223UVtby3vvvceVV17p/d3YsWMBuPbaa1EUxfszwNVXX80rr7xCR0fHsM/fcITUOmKbN2/mf//3f9m1axcVFRW8/PLLXHPNNed9zocffsgDDzzAoUOHyMzM5Lvf/S533313n21eeuklfvSjH3Hy5EnGjx/Pz3/+c6699lo//iVCCCGEEEIYr8PhYuqP3zbk2Id/egVRYRcOR2644Qbuuece/vWvf3HDDTcAUFtby+uvv85bb73FRx99RFRUFJMmTfI+Z8eOHaSlpbFu3To+85nPYLVavb+bN28eDoeD7du3s3z5ct//YQMUUiNibW1tzJw5k9/97ncD2v706dOsXr2aZcuWsWfPHr7//e9zzz338NJLL3m32bp1KzfddBO33nor+/bt49Zbb+XGG29k27Zt/vozhBBCCCGEEAMUGRnJzTffzLp167yPPfPMM2RnZ7NixQoKCwtJT0/vk5aYmpoKQEJCAhkZGd6fAaKjo0lISKCwsDBgf0N/QmpE7Morr+wz5Hghf/zjHxk9ejRr164FYMqUKezcuZNf/vKXrFmzBoC1a9eycuVKHnzwQQAefPBBPvzwQ9auXctzzz3n87/B394/WsO+OoWwgmrC7DYsFoW4CDuJUXaSosOIi7BjscgCfkIIIYQQAiLtVg7/9ArDjj1Qd911F/Pnz6esrIysrCzWrVvH7bffjqIodHR0EBERMbhjR0bS3t4+2Cb7VEgFYoO1detWVq1a1eexK664gscffxyHw4Hdbmfr1q3cf//9Z22jB2/96erq6pOj2tzcDIDD4cDhcPjuDxiC7798kLo2K08c29vv78NtFiZnxDItM478zDgumpDMqPjBvXBHMv3/1+j/Z7OS8+tfcn79S86vf8n59T85x/4VLOfX4XCgqiputxu32w1AhM2YJDlVVQc8T2zmzJnMnDmTJ598klWrVnHgwAFeffVV3G43SUlJNDQ0ePep/11An7+zt/r6epKTk/v93UC43W5UVcXhcPRJe4SB/x+bOhCrrKwkPT29z2Pp6ek4nU5qa2sZNWrUObeprKw8534ffvhhHnroobMef+edd4iKivJN44coI8xCnEXBreL96nBBmxO6XApdTjf7SpvYV9oEgIJKXrzKglSVmUkqYQPvmBjRNm7caHQTTE3Or3/J+fUvOb/+JefX/+Qc+5fR59dms5GRkUFrayvd3d2GtmWwbr75Zv7whz9w+vRpVqxYQXx8PM3NzUycOJHKykoaGxv7bG+322ltbfUOmuhOnz5NZ2cneXl5Z/1uoLq7u+no6GDz5s04nc4+vxvoSJupAzHQ1g/oTY+6ez/e3zZnPtbbgw8+yAMPPOD9ubm5mZycHFatWkVcXJwvmj1kK1c62LhxIytXrsRut/f5XbfTTVljBwfLmzlU3szu4kb2lDRxrEnhWBO8GmHjWyvGceui0ditITV9MGAcjnOfXzF8cn79S86vf8n59S85v/4n59i/guX8dnZ2UlJSQkxMzKDT+Yx2xx138OMf/5innnqK9evXe++7L7roIlJTU/n000+54YYbvPfxY8eOZevWrVx++eWEh4eTmJgIwJ49exg3bhwzZ84ccls6OzuJjIzk4osvPus8DjS4M3UglpGRcdbIVnV1NTabjeTk5PNuc+YoWW/h4eGEh4ef9bjdbg+aD67+2mK3w8TIcCaOSuC6udpjxXXt/HNPKS/tLqWkvoOH3zrGi7vLeejqfJZOSDGg5aEhmP6vzUjOr3/J+fUvOb/+JefX/+Qc+5fR59flcqEoChaL5YJrbgWbhIQE1qxZwxtvvMF1113nbb/FYuHLX/4yL774IjfeeKP38V/96lc88MAD/PWvfyUrK8tbnOOFF17grrvuGtbfb7FYUBTlHPfcA/v/Da2zP0iLFy8+a/j3nXfeYd68ed4TdK5tlixZErB2Gml0chT3XT6RD79zCY+smU5ydBgnqlu55a/beODve+l0uIxuohBCCCGEEABUVFRwyy23nDUocu+997Jp0yaKioq8j1111VUcP34ch8PhDcIOHjzI3r17+frXvx7IZvcrpAKx1tZW9u7dy969ewEtv3Pv3r0UFxcDWsrgbbfd5t3+7rvvpqioiAceeICCggKeeOIJHn/8cb7zne94t7n33nt55513eOSRRzhy5AiPPPII7777Lvfdd18g/zTDWSwKN80fzfvfWcHtS8ZitSj8c3cZX/zLp9S0dF14B0IIIYQQQvhJfX09zz//PO+//z7f/OY3z/p9eno6jz32mDcuOJfy8nKeeuop4uPj/dXUAQup1MSdO3dyySWXeH/W52l96UtfYv369VRUVPQ5+bm5uWzYsIH777+f//u//yMzM5Pf/va33tL1AEuWLOH555/nhz/8IT/60Y8YP348L7zwAgsXLgzcHxZE4iPt/OTqfK7Iz+Dup3exp7iRa/7vY564fT6TMmKNbp4QQgghhBiB5syZQ0NDA4888kifhZt7W7169QXrNZxZUd1IIRWIrVix4rwlLtevX3/WY8uXL2f37t3n3e/111/P9ddfP9zmmcri8cm8/I0lfGX9Dgrr2lnzh0948ivzmTsmyeimCSGEEEKIEcboxZf9IaRSE0VgjUuN4eVvLGVhbhKtXU7ueHInJ2tajW6WEEIIIYQQIU8CMXFeidFhrPvyfGbmJNDY7uD2ddtlzpgQQgghhBDDJIGYuKCoMBuPf2keo5OiKKnv4I4nd9De7bzwE4UQQgghhBD9kkBMDEhKTDhPfmUBiVF29pc2cc9ze887X08IIYQQQghxbhKIiQHLTYnmr1+aT5jNwrsFVTy97fzlQYUQQgghhBD9k0BMDMrcMYn852cmA/CLNwo4XdtmcIuEEEIIIYQIPRKIiUH78pKxLBmfTIfDxf0v7MXpchvdJCGEEEIIIby6u7uZMGECH3/88Xm3+853vsM999wToFb1JYGYGDSLReGXN8wkNsLG3pJG/vjhSaObJIQQQgghRqj169dz6aWX9nnsz3/+M2PGjGHp0qWAtg6Zoijs3bu3z3bf/e53WbduHadPnw5Uc70kEBNDkpkQyU8/nw/A2nePc7CsyeAWCSGEEEIIoXnssce48847L7hdWloaq1at4o9//GMAWtWXBGJiyK6ZlcXq6Rk43So/+dchqaIohBBCCBFqVBW624z5GuC941NPPUVycjJdXX3Xsl2zZg233XbbWdvv3r2bEydO8NnPftb7WG5uLgCzZ89GURRWrFjh/d3VV1/Nc889N4STNzy2gB9RmIaiKPz4c/l8cKSGnUUNvHmwktXTRxndLCGEEEIIMVCOdvhFpjHH/n45hEVfcLMbbriBe+65h3/961/ccMMNANTW1vL666/z1ltvUVRU1Gf7zZs3M3HiROLi4ryPbd++nQULFvDuu++Sn59PWFiY93cLFiygpKSEoqIixowZ46M/7sJkREwMS0Z8BF9bPg6Ah98soNPhMrhFQgghhBDCTCIjI7n55ptZt26d97FnnnmG7OxsVqxYwe23387777/v/V1hYSGZmX2Dy9TUVACSk5PJyMggKSnJ+7usrCzv8wJJRsTEsH314nE8t72YkvoOnvykkK8tH290k4QQQgghxEDYo7SRKaOOPUB33XUX8+fPp6ysjKysLNatW8ftt9+OoihnbdvR0UFERMSA9x0ZGQlAe3v7gJ/jCzIiJoYtKszGf1yhrS32u/dPUNfadYFnCCGEEEKIoKAoWnqgEV/9BFHnMnv2bGbOnMlTTz3F7t27OXDgALfffnu/26akpNDQ0DDgfdfX1wM9o2aBIoGY8InrZmcxLSuOli4na989bnRzhBBCCCGEydx5552sW7eOJ554gssvv5ycnJx+t5s9ezZHjhzpU0hOnxPmcp09jebgwYPY7Xby8/P90/BzkEBM+ITFovCD1VMBeHZ7MSX1gR3aFUIIIYQQ5nbLLbdQVlbGX/7yF77yla+cc7tLLrmEtrY2Dh065H0sLS2NyMhI3nrrLaqqqmhq6ll6acuWLSxbtsybohgoEogJn1k8PplleSm43Cp/3nzK6OYIIYQQQggTiYuLY82aNcTExHDNNdecc7vk5GSuu+46nnnmGe9jNpuN3/72t/zpT38iMzOTz3/+897fPffcc9x1113+bHq/JBATPvX1FVqhjr/vLKGmReaKCSGEEEII36moqOCWW24hPDz8vNt9//vf54knnqClpcX72J133klxcTEul4tNmzYB8MYbb2C1Wrn++uv92ex+SSAmfGrxuGRmj06gy+nmiY9PG90cIYQQQghhAvX19Tz//PO8//77fPOb37zg9tOnT+d//ud/LliSvq2tjXXr1mGzBb6YvJSvFz6lKArfWDGBu57aydNbi/j6ivHERdiNbpYQQgghhAhhc+bMoaGhgUceeYRJkyYN6Dlf+tKXLrjNjTfeONymDZkEYsLnLpucxsT0GI5VtfK3rUV885IJRjdJCCGEEEKEsEAvthwIkpoofM5iUbxzxdZ9fJpOx9llQoUQQgghhBjJJBATfnHVjEyyEyOpbe3mxZ0lRjdHCCGEEEKIoCKBmPALm9XCXcvGAfDU1qI+C+oJIYQQQggx0kkgJvzmujlZRIVZOV7dyo7CBqObI4QQQgghRNCQQEz4TWyEnc/PygTgmW1FBrdGCCGEEEKI4CGBmPCrmxeMAeDNA5XUtcoCz0IIIYQQQoAEYsLPpmfHMzM7nm6Xm3/sKjW6OUIIIYQQYgSqq6sjLS3tgmXwr7/+en79618HpE0SiAm/u2WhNir27PZi3G4p2iGEEEIIIXxn/fr1XHrppd6ff/KTn3D77bf32ebhhx/mqquuYuzYsQBs2rQJRVFobGzss92Pf/xjfv7zn9Pc3OznVksgJgLgczNHERtho6iunY9P1hrdHCGEEEIIMYJ0dHTw+OOPc+edd15w2xkzZjB27FieeeYZv7dLAjHhd1FhNtbMyQbgmU+LDW6NEEIIIYTQqapKu6PdkK+BLm/01FNPkZycTFdX33oDa9as4bbbbrvg8998801sNhuLFy8GoLCwkEsuuQSAxMREFEXpM4J29dVX89xzzw3wDA6dze9HEAK4eeFo1n9SyMaCKqpbOkmLjTC6SUIIIYQQI16Hs4OFzy405Njbbt5GlD3qgtvdcMMN3HPPPfzrX//ihhtuAKC2tpbXX3+dt956i6Ki81fn3rx5M/PmzfP+nJOTw0svvcSaNWs4evQocXFxREZGen+/YMECHn74Ybq6uggPDx/iX3dhMiImAmJieiyzRyfgcqu8vq/C6OYIIYQQQogQERkZyc0338y6deu8jz3zzDNkZ2ezYsUKbr/9dt5//33v737yk5+wfv1678+FhYVkZmZ6f7ZarSQlJQGQlpZGRkYG8fHx3t9nZWXR1dVFZWWlH/8qGRETAXTNrCz2FDfyyt4yvnJRrtHNEUIIIYQY8SJtkWy7eZthxx6ou+66i/nz51NWVkZWVhbr1q3j9ttvR1GUCz63o6ODiIiBZ2Ppo2Pt7e0Dfs5QSCAmAuazM0bx09cPs7+0iVM1rYxLjTG6SUIIIYQQI5qiKANKDzTa7NmzmTlzJk899RRXXHEFBw4c4LXXXhvQc1NSUmhoaBjwserr6wFITU0dUlsHSlITRcCkxIRzcV4KAK/sLTe4NUIIIYQQIpTceeedrFu3jieeeILLL7+cnJycAT1v9uzZHD58uM9jYWFhALhcrrO2P3jwINnZ2aSkpAy/0echgZgIqGtmZwHw6t6yAVfKEUIIIYQQ4pZbbqGsrIy//OUvfOUrXxnw86644goOHTrUZ1RszJgxKIrC66+/Tk1NDa2trd7fbdmyhVWrVvm07f2RQEwE1Mqp6USFWSmqa2dPSaPRzRFCCCGEECEiLi6ONWvWEBMTwzXXXDPg502fPp158+bx97//3ftYVlYWDz30EN/73vdIT0/nW9/6FgCdnZ28/PLL3HXXXb5u/lkkEBMBFRVmY9XUdABe3VNmcGuEEEIIIUQoqaio4JZbbhl0Wfkf/ehHPProo7jd7j6PVVRU4Ha7vVUWH3/8cRYuXMiiRYt82ex+SSAmAu7znvTE1/dX4HC5L7C1EEIIIYQY6err63n++ed5//33+eY3vzno569evZqvfe1rlJWdfyDAbrfz2GOPDbWZgyJVE0XALZuQQnJ0GHVt3Xx0opZLJqUZ3SQhhBBCCBHE5syZQ0NDA4888giTJk0a0j7uvffeC27z1a9+dUj7HgoJxETA2awWrpqZyfpPCvnX3nIJxIQQQgghxHkVFhYa3QSfk9REYYjPzRgFwLsFVXQ7JT1RCCGEEEKMLBKICUPMGZ1ISkw4LZ1OPj1VZ3RzhBBCCCFGFFlGaHh8cf4kEBOGsFgUVuVr1RPfOlRpcGuEEEIIIUYGq9UKQHd3t8EtCW3t7e2AVtxjqGSOmDDMFfkZPLutmI2Hq/jvz0/DYlGMbpIQQgghhKnZbDaioqKoqanBbrdjsZhjXMbtdtPd3U1nZ6df/yZVVWlvb6e6upqEhARvYDsUEogJwywel0xshI2ali72lDQwd0yS0U0SQgghhDA1RVEYNWoUp0+fpqioyOjm+IyqqnR0dBAZGYmi+L9zPyEhgYyMjGHtQwIxYZgwm4XLJqfxyt5y3jpYKYGYEEIIIUQAhIWFkZeXZ6r0RIfDwebNm7n44ouHlS44EHa7fVgjYToJxIShrsjP4JW95bx9qIrvr54SkB4MIYQQQoiRzmKxEBERYXQzfMZqteJ0OomIiPB7IOYrIZcU+vvf/57c3FwiIiKYO3cuW7ZsOee2t99+O4qinPWVn5/v3Wb9+vX9btPZ2RmIP2fEWz4plXCbheL6dgoqWoxujhBCCCGEEAERUoHYCy+8wH333ccPfvAD9uzZw7Jly7jyyispLi7ud/tHH32UiooK71dJSQlJSUnccMMNfbaLi4vrs11FRYWpegiCWVSYjYsnpgLwtlRPFEIIIYQQI0RIBWK//vWvueOOO7jzzjuZMmUKa9euJScnhz/84Q/9bh8fH09GRob3a+fOnTQ0NPDlL3+5z3aKovTZbrgT78TgfCZfO98SiAkhhBBCiJEiZOaIdXd3s2vXLr73ve/1eXzVqlV88sknA9rH448/zuWXX86YMWP6PN7a2sqYMWNwuVzMmjWLn/3sZ8yePfuc++nq6qKrq8v7c3NzM6BNEnQ4HAP9k/xCP77R7RiMiyckYbUoHKls4URVE2OSooxu0jmF4vkNJXJ+/UvOr3/J+fUvOb/+J+fYv+T8+lcwnd+BtkFRQ2RZ7fLycrKysvj4449ZsmSJ9/Ff/OIXPPnkkxw9evS8z6+oqCAnJ4dnn32WG2+80fv4p59+yokTJ5g+fTrNzc08+uijbNiwgX379pGXl9fvvn7yk5/w0EMPnfX4s88+S1RU8AYRwex3hywcb7awZqyLi0eFxEtSCCGEEEKIs7S3t3PzzTfT1NREXFzcObcLmREx3ZlV9VRVHVClvfXr15OQkMA111zT5/FFixaxaNEi789Lly5lzpw5PPbYY/z2t7/td18PPvggDzzwgPfn5uZmcnJyWLVq1XlPdiA4HA42btzIypUrQ6ZiDEB5XCGPvH2MGnsaq1fPNbo55xSq5zdUyPn1Lzm//iXn17/k/PqfnGP/kvPrX8F0fvVsuQsJmUAsJSUFq9VKZWXfeUTV1dWkp6ef97mqqvLEE09w6623EhYWdt5tLRYL8+fP5/jx4+fcJjw8nPDw8LMet9vthv/H64KpLQNx+dQMHnn7GNtON+BULUSGDX9tBn8KtfMbauT8+pecX/+S8+tfcn79T86xf8n59a9gOL8DPX7IFOsICwtj7ty5bNy4sc/jGzdu7JOq2J8PP/yQEydOcMcdd1zwOKqqsnfvXkaNGjWs9orBmZAWQ1ZCJF1ON5+eqjO6OUIIIYQQQvhVyARiAA888AB//etfeeKJJygoKOD++++nuLiYu+++G9BSBm+77baznvf444+zcOFCpk2bdtbvHnroId5++21OnTrF3r17ueOOO9i7d693nyIwFEVhxSStjP0HR6sNbo0QQgghhBD+FTKpiQA33XQTdXV1/PSnP6WiooJp06axYcMGbxXEioqKs9YUa2pq4qWXXuLRRx/td5+NjY189atfpbKykvj4eGbPns3mzZtZsGCB3/8e0dclk9J4Zlsx7x+p5qGrBzb3TwghhBBCiFAUUoEYwDe+8Q2+8Y1v9Pu79evXn/VYfHw87e3t59zfb37zG37zm9/4qnliGJZMSCbMaqG0oYOTNW1MSIsxukmmU91ezcHagxysPUi7s5256XNZkLGA+PB4o5smhBBCCDGihFwgJswrKszGwnFJbDley6aj1RKI+dBbhW+xdtdaylrL+jz+TMEzWBQL+cn5fHHyF7lq/FUGtVAIIYQQYmSRQEwElRWT0jyBWA13LhtndHNCXkNnAz/f9nPeLnwbAItiYVz8OKalTCPSFsm2im2cajrFgdoDHPjoAFvLt/LDRT8kyi7r4QWjkzWt1Ld1kxQdRkp0OHGRNknhDREut4sOZwcdzg7CrGHmGoXubof6kxCbCVFJYLLXZJfTxenaNqLDbOQkyWdjqHC5XRxvPE60PZrsmGzzfVa6nNBaCd1tkJwHlpAq+3BOqqpS0dSJ3WohLtJGuC24q2gPlwRiIqhcMimVn70O207X0dblJDpcXqJD9VHZR/zwox9S11mHVbFy5/Q7+cq0r5wVZFW2VfLy8Zf54/4/8tqp1zhQe4BfLv8lk5ImGdTyIWqvh5PvQ3MZNJVBSzkk5sLML0B6vtGtG7L2biev76/gue3F7Clu7PO7CLuFL8wfzb2X5ZEYff6lOUTgtTvaefbIszx9+GnqOnuqwVoUC5fmXMq/Tf035qTNCc0bRGc3nPoADvwDjm6A7lbt8bBYSBoL4y+Di78D4bGGNnOodhbWs+6TQo5UNFNY147LrQIwMT2GVVMzWJWfzvSs+ND8vzOxdkc7H5Z+yObSzXxU9hGNXY0AxNhjmJQ0iRkpM7h92u0kRSQZ29ChqjoE7/83VOyHlgpQXdrjCaNhxhe0613yeGPbOETt3U5e2VPO3z4toqCiZw2uCLuFSRlx3H95HssnppruPSd3uSKo5KZEMyY5iqK6dj4+Ucuq/AyjmxSStlVs49vvfxun28m4+HH84qJfkJ/SfzCSEZ3B12d9nQWjFvDdzd+lsLmQWzbcwh8u/wPzM+YHuOVD4HbBrnXw3s+gs/Hs33/yWxg1E2bdAnNvB9vZawAGq2e3FfPwhgJaupwA2CwKmQmRNLR109LlpNPhZv0nhfxzdyn3XJbHbYvHEmYzR69oKOt2dfPiiRf58/4/9wnAABQU3Kqbd4vf5d3id5mSNIV759zL0qylBrV2CAo/hn98ReuN14XHQVczdLdA5QHta//f4TMPw9TPh8woWWuXk/996whPfVqEqvY8Hhtuo8Ph4lhVK8eqTvC7D07w2emjeOT6GcRIh2FQOFR3iH/f9O99UvCj7dF0u7ppdbSyq2oXu6p28cqJV/jBoh9wxdgrDGztIHU0wqaHYftfeoIvAItN+2oshs3/o33lXQHX/lEbnQ4BTpebte8e58mthbR0atc6q0Xxdn50OtzsK2nk9nU7WDI+mQevnML0bPNkFMinhwgqiqKwYmIqT24t4oOjNRKIDcHxhuPc98F9ON1OVo5ZycPLHibceuHgY276XP5x1T/43pbv8Un5J9y/6X6e++xz5MTmBKDVQ1S8DTb8u3bTB1p6RuZsiM+CmHQo/AiOvQ0V+7SvQy/DTc9AdLKx7b4At1vlkbeP8KcPTwEwOimKLyzI4Ya5OaTGav+XXU4X20/X8/M3CjhS2cJ/v1HA33eW8PQdC0mLizCy+SNau7udL2/8MgX1BQBkxWTxzVnf5KKsi4i0RRJuDedk40meOfIMr518jYL6Ar7x3jf4r8X/xXV51xnc+gHY9SS88e/gdkB0GuRfC9Ovh+z54OyCxiLt/fj+f0PDaXjxSzBhJVzze4hJM7r157XleA3fe+kAZY0dAFw/N5urZmYyMT2GjLgImjucfHC0mncOV7LxcBVvHKjgWFULf7p1LuNSZU6zUVRV5Z/H/8kvtv2Cbnc36VHprM5dzbLsZcxKmwXA6abTFNQV8OThJznecJzvfPgdNhZt5PsLvx/8o2PH3oFXvwFtNdrPU66Gxd+EhDHae8rZpY1K73tOywo5/jY8vhJu/nvQj451dLv49nO7ebdAW7ZoTHIUty4aww1zc4iJsNHa5aS+rZtntxXx5CdFfHKyjqt+9xH3XJbH/ZfnmWJ0TFHV3n0+Yiiam5uJj4+nqamJuLg4Q9vicDjYsGEDq1evNnxV8aH64Eg1X16/g5ykSLZ891Kjm9NHsJ/fqrYqbtlwC1XtVcxJm8OfV/15QEFYb53OTr781pc5WHeQCQkT+NuVfyMmLDA3GYM6v/tfhH/eBagQEQ+X/gjmfhmsZ/QvtdXBwX9oN4ZdzVq64s1/h9SJfvs7hqPL6eI7L+7ntX3lANx/+US+fekELJb+Lzgut8qLO0v45TtHqW3tJi8thue/uojkmLP/34P99RvqaltrueWVWyh3lZMQnsC3Zn2L6/Kuw27t/1w3dDbwq52/4tWTrwJw75x7uWPaHcF5c+Fywjs/hG1/0H7OvxY+/3sIO8ecKUcHbPk1fLwWXN2QPg1ufwMiE4bcBH++ft8+VMnXn96FW4XsxEj+33UzuCgv5Zzb7yqq5+tP76a6pYvYcBtrvzCLy6ak+7RNRgi1z4huVzc/+/RnvHLiFQBWZK/gvy/673POwex2dfOn/X/i8QOP41Jd5MTm8Lcr/0ZyZGA65wZ9fk++D8/epL2HkvPgykdgwmXn3r7yIDz3BWgqgYgEuOlpyF3ms/b7Un1bN3c8uYM9xY2E2yz8z/UzuGpG5jmvdSX17fzqnaO8sle7Nt53eR73Xd73Oh5Mr9+BxgaSwyKCzoLcJGwWhZL6Dorrzr30gOirzdHGN9/7JlXtVYyNG8tvL/3toIMwgAhbBI9e+ihpkWmcaDzB97Z8D5fbdeEnBtLRt+DlrwEqTLsevr0bFtx1dhAG2ujXwq/BHRu1PPqG0/D45XB6c8CbfSGdDhe3Pb6d1/aVY7Mo/PKGmdx7ed45L0ygpXB8YcFoXvr6EtLjwjle3cqtj2+nqd0RwJaLpq4mvv7+1yl3lZMYnsi6K9Zx0+SbzhmEASRGJPKzpT/jzul3AvDo7kf5nx3/g1t1B6rZA6Oq8M87e4KwS34A1687dxAGYI+ES38AX9uijU5XHYTnvqgFaEFmV1E99zy3B7cK18zK5O37Lj5vEAYwd0wSr3/7IuaNSaSly8ldT+3ko+O1AWqxAG0k7L8//W9eOfEKFsXCvXPu5dFLHz1vIZwwaxjfnv1tnvnsM2TFZFHSUsI9799DhzP4XpeUbIfnb9GCsClXw9c/OX8QBpAxDe58D7Lmaan6f7sGjrwRiNYOSlljB9f/8RP2FDcSH2nnmTsX8vlZWee91uUkRbH2C7P54WenALD23eP83wcnAtVkv5FATASd6HAbc0YnAvDRCbmwDdT/7vhfjjYcJTkimT9c/odhVWVLi0rj0UsfJdwazoelH/J/e//Phy0dpsKPtHQn1QUzvwjX/QWiz3/TBEDaZLjzfcheAJ1N8MyNUHXY/+0dhJ+9fphtp+uJDbfx5FcWcP3c7AE/d0xyNM/cuYiUmDAOVzTzpXXbafXMLRP+1dzdzF3v3MWRhiNEK9H8+bI/MyFxwoCeqygK9865l/+Y9x8APF3wNE8cfMKfzR28rb/T0notdrjxKVj+3YHP+UqbDP/2EoTHQ/En8OKXwRU8nQQnqlu548mddDndXDY5jV/eMHPARaLS4iJ49q5FfH5WJm4Vvv3cbkrqpfMwUF44+gIvn3gZi2Lh0Use5c7pd2JRBnZbm5+c771O7q/dz/c2B1mHY+VBeOZ6cLTD+EthzV/BNsCCTLHpcPvr2qi12wkv3w11J/3b3kHocrr46lM7OVXTRlZCJC99fTHzxg48PfTOZeP4z89MBuB/3z7Knz4Mnr9tKCQQE0Fp6QTtxvpjCcQGZFfVLl46/hIAv1z+S7JjB34Dfy7TUqbx0yU/BeDxg49zpP7IsPc5bOV74NkvgLMTJn0Wrv7d4Er2xqTCl16D3OXg7NACuq5W/7V3EF7fX84z24pRFPi/W+Z43wODMSEthr/dsZCEKDt7Sxr5jxf3Idnn/vfzT39OQX0BieGJfCXmK4xPGPy8jNvyb+NHi34EwO/2/I79Nft93cyhKfoENv6X9r1eeGOwMqbDzc+DLQKOvQmv3QdB8Lqsbu7kS09sp7HdwaycBB67eTY26+Bui8JsFh5ZM4PpWfE0tDu4++lddDqC6IbepHZV7eKR7Y8AcN+c+1iRs2LQ+8iNz+W3l/yWMEsY75e8z//s+J/g+LxsroCnr9M6DHMWaumFgy0yZY/UOilzFmkp+S9+KWhGo//fm0c4VN5MUnQYf797MRPSBl9Z9esrxvPASi0t8eE3j/DB0WpfNzNgJBATQemiPC1f+5OTtbjdQfDBGMS6Xd38dKsWMK3JW8O8jHk+2/fqcau5YuwVuFU3v9j2C2NTpjqb4fl/06qy5V4M1z/RfyrihdgjYM3jEDsKao/B6/cbflNYVNfG917SCo58Y8V4Lp6YOuR9TRkVx7rb52OzKLx5sJI3DlT4qpmiH+8UvsOG0xuwKlbWLl9LunXo84RumHgDnxn7GVyqi+9u/i6t3QZ3ErRUaSNYqgum3wDz7xz6vsYsgRvWg2KFvU9Dwb981syhUFWV+17YS1ljB7kp0Tz+pXlEhQ2tflmE3cofb51LUnQYh8qb+f7LB4Ljht6kKtsqeWDTAzhVJ1eOvZLb828f8r7mpM/hF8t+AcCzR571ztc0jKpq16TWKkibCje/AGHRQ9uX1a5dJ6OStQI6b/6nb9s6BO8ermLdx4UA/PKGGWQlRA55X1ql4DEAfO+l/SGbji+BmAhKM7ITiA6z0tDu4HCv9STE2Z44+ASnmk6RFJHE/XPv9/n+vzPvO0TaItlTvYfXTr7m8/0P2HsPQXMpJI6FLzyrBVRDFZOqXaAUKxz4O+x+0mfNHKwup4tvPbuH1i4n88cmcv/lwy8iMnt0It+4REuN+/Grh6ht7Rr2PsXZajtq+dmnPwPgjul3MD1l+rD2pygKP1r8IzKjMylrLeO/t/23L5o5NC4nvHSHVqI+dTJc9ejwS9BPuhKWPaB9v+E/oKNh+O0con/sKuWTk3VE2C08cfv8fovbDEZWQiS/++JsLAr8c3cZz20v8VFLRW9u1c13PvwO9Z31TE6azENLHxp2cZsrxl7Bt2Z9C4Bf7vwl9Z31vmjq0Bx8SRs1tniCqMjE4e0vPktLa0TRrnP7nvdJM4eioqmD//jHPgDuuCiXSycPv7jNg1dOITclmqrmLn76enBNNRgoCcREULJbLSwap42KSXriuRU2FfKX/X8B4D/n/+ew5oWdS0Z0BnfPvBuAX+/6Nc3dBgTGhR/Djr9q31/9mG8WiR2zBC77sfb9hu9qOfkG+PU7xzhQ1kRilJ3ffnHwqVHn8q1LJjA5I5b6tm7+69VDPtmn6KGqKg9tfYjGrkYmJ03m7hl3+2S/cWFxPHLxI1gVK2+cesO4zo+dj0PhFgiLgRv/NvRe+TNd/B+QMlHr8X/nh77Z5yDVtXbx8w3a8gL3XT6R3BTf/G1LJqR45648/GYBddIB4nOvnniVfTX7iLZHs/aStUTahj6i0ttXpn+FSYmTaOpq4lc7f+WTfQ5aWy28+V3t+4v/A9Km+Ga/4y+F5Z7RsNfvh6ay82/vB263yn3P76Wh3cG0rDi++5lJPtlvZJiVX94wA4sCL+0u5b0joZeiKIGYCFr6HBkp2NE/vWJUt7ubpZlLuTL3Sr8d69Ypt5Ibn0t9Zz3/tyfAhTscHfCvb2vfz/mSlpboK0vugYmfAVcXbPhOwFMUC2vbeOLj0wD8z/UzGRXvm5sK0Oau/PKGmVgtCm8cqOCN/ZKi6EuvnnyVTSWbsFls/Pyin5+3OuJgzUqb5e38eHj7wzR1Nfls3wPSXg8faOlarPypb5d6sIVrnSkosOdpOLXJd/seoJ+/UUBju4PJGbHccVGuT/d957Jx5GfG0dLp5JfvHPPpvke65u5m1u5eC8DXZ36drJgsn+3bbrHz48U/RkHhXyf/xfaK7T7b94C9+Z/QXgdp+XCRj7Nbln9Xm2/maIf3f+bbfQ/Ay3vK2Ha6nugwK499cQ7hNqvP9j13TBJ3LRsHwI9ePUxbiGUoSiAmgpZePnhHYT1dTpn8fKZPKz5lW+U2wixh/GDRD/y69pDdauf7C78PwPNHn+do/VG/Hessm/4f1J/U5nSt8vEFxGKBz/0GbJFQvDXg81YeeesIDpfKikmprJzq+zWIpmXF880VWuGIH796kKaOELtCBammrib+Z8f/APDNWd9kYqLv16S7a/pdTEycSEt3i3fUO2A+fEQrfZ2Wr3V++NroRT3zzV67F7oDV2lwy/Ea/rmnDEWB/7dmBnYfjUDrrBaF/7oqH4DndxRzsCzAQbSJ/WHvH6jvrCc3PpebJ9/s8/3PSJ3BjZNuBOBnn/6Mble3z49xTkff1Na7VCzw+ccGXiFxoCxWrdgOaAs/l+327f7Po9Ph4lfvaPcM374sz2cj0L3dv3IiE9JiqGnt5qXC0AptQqu1YkTJS4shNTacToeb3UWNRjcnqKiqyh/3/RGAGybdQE5sjt+PuWjUIlaOWYlbdfOn/X/y+/EAbYLxJ49p33/uN9rCzb4WlwlL79W+3/hjcAYmnWhnYT1vHqzEomh57v7yrUvzyEuLoa6tm8c9k6TF8Dx56ElauluYkDBhWIUCzsdqsXrnfD575FnKWgOUTlRzFLZ7Ar/P/GJoBXEG4vL/grhsaCiELYFJBet0uPjhK1oK8pcWj2VWToJfjrMgN4mrZmaiqvDT1w5L4Q4fONl4kueOPAfA9+Z/z6cj0L3dM+ceUiJTKGwu5PEDj/vlGGdxOXoKaSz+FmTN9c9xsubCjJu079/+QcAyQJ74+DTlTZ1kJURy+5KxfjlGhN3KrzwZIFYFnK4gW4vxPCQQE0FLURSWjpd5Yv3ZUbmD3dW7sVvsfDn/ywE77tdnfh2Ad4ve5XTTaf8f8P2faxXbpn5em+jvL0vvgZgM7aZw+5/9dxwPVVX57ze0OSo3zc9hUoYP5rydQ5jNwr+v0vLxn9paTKsMig1LbUctTxc8DcC3Zn8Lm8VPgQqwNHMpCzMW4nA7+N2e3/ntOH2880PtPTdpNYxb4b/jhMf29NBv+6OWDulnz28vpqiunYy4CP59le9HMXt78MrJRNgtbC+s53VJCx4WVVV5ePvDuFQXl+ZcypKsJX47VlxYHP85XwuKHj/4OLUdAbj32PssNBZBdBqs+J5/j3XZjz0ZIJ8EJAOkrrWLP3ygrfP176smEmH3XUrimWbmJPDmt5dwywS3z+ZaB0LotFSMSDJPrH9/3K+Nhq3JW0N6tO9T2s4lLzGPFTkrUFH9v+hs2W6tepRigUt/7N9jhUX3FO748H+hrc6vh3t9fwV7SxqJCrNy/0r/3hACXJGfzrSsONq6XbxXLh/7w/H4gcfpcHYwLXkal+Zc6tdjKYrC/fO0UbE3Tr1BQV2BX4/H8Xfh+DtaxbZVAajYOOUqbY2x7lZt0Wg/6nK6+OOHpwD41qUTiI3wz4iKLjMhkq8v1yqXPryhgI5uSa8fqk0lm9hWoaXhf2f+d/x+vCvGXsGM1Bl0ubpYf3C9fw/m7IbNv9S+v+g+3xXFOZf4bFjimXMdgAyQx94/QUuXk/zMOK6Z5bs5fefij7RHf5MrsghqeiC2v7RR5rd47KzcyY7KHdgtdu6YfkfAj3/ndG1ux+snX6eyrdJ/B/pQW6yT6TdCygT/HUc384uQMQO6muDD/+e3w3Q5XTzylrY49t3Lx5MWO4wy/AOkKIp38cstlQo1LVLNbSgqWit44egLAHx7zrf9Oi9Tl5+cz5W5V6Ki8ptdv/HfgVRVuzEDWPg1SB78otSDpiiw4kHt+21/8uuo2Is7S6ls7iQjLoIb5g1/wfuB+NrycWQlRFLe1MmLu6Sc/VCoqsqf92tZCrdOvTUgafiKovC1GV8D4O/H/u7fcvZ7n4GmYohJh3lf8d9xelt6b68MEP/NPy2sbePpT4sA+P7qKVgs/v+8DEUSiImglpkQybiUaNwqbD9t4NoeQUQfDbt2wrVkRGcE/PgzU2eyIGMBTtXJk4f8s/6WUr4bjr2ljYYt/65fjnEWiwWu+Ln2/Y7HobHYL4d5eXcZpQ0dpMeFc+cy31ZsO59LJqUxMzseh1vhT1sCkFZqQn/a/yccbgfz0uexeNTigB33ntn3YLPY2Fqxla3lW/1zkBPvQvUhrVz9xf4fdfCatNrvo2LdTjd/2KSlR319xXifVmw7nwi7la8t16q5/WXLqZCatxIsdlTu4GDdQcKt4dw69daAHXdZ1jKmJk+lw9nB3w7/zT8HcXb1zI+86H6w+65q7nmFx8AlWvEttv5OG5Xzg9+8ewynWytGpXeqi7NJICaC3kLPemLbTvk3XSwU7Knew7aKbdgsNu/IlBH0Y//j2D/80lto2axVpGPGTYHpmdflXgy5y7U5Mtt8X5BEVVX++pEWBN21bBxRYf6bX3QmRVG47zJtZPG5HaVUNHUE7NhmUNRcxCsnXgG0Cf2BGA3TZcdmc9MkbZL9uoPr/HOQjx/V/p3zpeEvIjsYigLLPfNi/DQq9s/dpZQ1dpAWG85N8/0/otLbDXNzSIoOo6S+gw0H/ZhBYFJ6Cvy1E64lOTI5YMdVFIWvzvgqAM8dec4/S0js+Rs0lWijU3Nv9/3+z2fmF7TjtlRoi0j7WHljh3du5HdW+WbNMLOSQEwEvUXjkgDYJiNi3hGoz4//PKNiRhnWjkWjFpGfnE+nq5NnCp7x6b4T205iOfkuKFZtUctAW3KP9u+u9dDR6NNdbzpWw4nqVmLCbdwY4BtCgKXjkxgfq/YZIRADs+7gOlyqi2VZy5idNjvgx7916q1YFAtbK7ZyouGEb3devkdbvFmxwqKv+3bfAzH5s34bFXO43PzfJu18fW35eL8WC+hPZJiVLy0eC8CfPjwpFRQHoaCugI/LP8aqWPlSvh+WUbiAS3IuIS8xjzZHG88WPOvbnTu7YMuvte+XPRC40TCdLVxLQQbtPefj1+WTnxTicqssHpfMtCw/VDs2EQnERNBbmKv1gh0qb6K5c+TOE6tqq2JTySYA/m3KvxnaFkVRvKNizx15jk5np8/2PbHyFe2bmV8I7GiYbsJlkDpFuync7dvUy79u0YoFfGF+DnF+LhbQH0VRuCJHS4/6x67SEf1+GoymribeOPUGAF+ZFqB5HGfIisnistGXAXirNvqMvkTEtDWQEPgOgrNGxXzYAfLKnjJK6jtIiQnj5gWjfbbfwbht8Rgi7VYOlTdL4alB0Ed/rxh7BdmxgZnX15tFsXhHxf5W8Ddau1t9tm/l4IvQXAaxmf5Zq28g5n0Z7NFQdRBOfeCz3bZ1OXl2u5ba7+sF081IAjER9DLiIxiTHIVbhV2FDUY3xzAvHX8Jl+pibvpcJiQGoHjFBVw6+lKyYrJo6W5hY9FG3+y07gQZzftQUWDZv/tmn4OlKLDkW9r3n/7RZ/nzh8ub+fhEHRYFbl861if7HIqJcSoTUqNp73bx0q5Sw9oRSl4+/jKdrk4mJk5kbrqf1vgZAH2OzGsnX/NdSnBDERx6Rfter6ZmhMmf7ekA2fe8T3apqip/8XR+3LVsHJFhgR0N0yVGh3lTIv/kqdwozq+kpYS3i94GjOv8AFg5eiW58bm0dLfw4rEXfbNTVcW607NG2cKvgd3/BZv6FZkIczzz7vTOGB94cWcJLZ1OclOiuXRyms/2a1YSiImQsDBXS0/89PTInCfmcDt46ZiWx63PFTGaRbFw7YRrAW2umE/2uXs9AOqElcaMhumm36BVsWoph0Mv+2SXf/1IuwG7cvooshOjfLLPoVAU+LeF2k3h3z4tklSpC3C5XTx/VAsMbp58c0Dnhp1pVuos8pPz6XZ38+JRH90Ufvp7bU7kuBUwaoZv9jkUigLzPVVgdz7uk1SpHYUNHKtqJdJu5YsLjRkN0925LBerReGjE7UcKPXDfCOTefLQk7hVN0uzljIpybg5RlaL1btW59+P/h23OvyCK4ntp1CqDoA1HGYHrgBJvxZ9XSuKdfJ9qDw47N253CrrPikE4CtLx0qlxAGQQEyEBD09cdupkTlPbFPJJqo7qkmKSOLy0Zcb3RyvayZcg1Wxsrt6N6eahtnT292OZb92w+uea1wPKKDlzy+4S/t+62PDvimsau7ktX3lgNYzb7TPz8okJtzGqZo2Pj4xMjs3Bmpz6WbKWsuIC4tj9bjVhrZFURTvqNjzR5+n2zXM0dr2etjtqQinz4000oybtFSp2mPanLVhemabVjr787MyDUkF7i07MYqrZmjzevVROtG/5u5mb2GcO6YFfomWM10x9gpi7bGUtpbyacWnw97f2Np3tW+mrYHowBUg6VfiWJj6ee37rf837N29W1BFUV078ZF21swNfDppKJJATISEhZ6CHQfKmmjrchrcmsDT1y5ak7cGu9XYG4re0qPTWZa9DMA7Yjdkh/6J0tlIW1gq6nj/LpQ7IPPuAHsUVB6A0x8Oa1dPflKIw6Uyb0wis3ISfNO+YYgJt7Fmjra45pNbC41tTJB79og2SX9N3hoibQGeUN+PVWNXkRaZRm1HLW8VvjW8ne19BhxtkD4NguE9FxEHM27Uvt/x+LB2VdfaxZsHtCqFtywcM9yW+cSdnk6Ytw5W0tDmn5LhZrDh1Aa6XF1MTJzIvPR5RjeHKHsUnxv/OYDhj0S315HVsF37fr5xlY/70FOSD7wILVXD2tXjnqrANy8cHdCqwKFMAjERErITo8hKiMTlVtlVNLLmiRU2FbKtYhsKCtdPvN7o5pzl+jytTf86+a/h9dDv+CsAhSmXaqkSRotKglm3aN8PY9FLh8vN33dqi7kGct2wC7l1sXZz+l5BFaUN7Qa3JjidajzFpxWfYlEs3DQ5OFKC7RY7X5zyRQCePvz00FNLVRX2eIp+zL9DSw0MBnp64pHXoWXo5d7/sauUbpebGdnxTM8Ojqpt07LimZYVR7fLzct7yoxuTtD65/F/AnBd3nWGpgL3dsPEGwD4oOQDatprhrwfy75nsaoO3BkzIWuOr5o3PFlzIXsBuB2wf+jzMw+VN7H9dD02i+KtFCouLAjudoQYmEX6emIjbJ7Y34/9HYCLsy8mMybT4NacbWnWUtKi0mjsauT94veHtpOyXVC+B9UaTnHyxb5t4HDM86RIHnsLWod28f3gSDW1rd2kxIRx2ZR0HzZueCakxbJkfDJuFZ7Z5p/Fq0OdPhq2PHs5WTFZBremx/V51xNmCaOgvoCC+oKh7aRsF9QcAVuEliIVLDKmQ85CcDth91ND2oXbrXqrtv1bkIyG6W6ap83P/PvOEpmf2Y/DdYcpqC8gzBLG58Z9zujmeOUl5jE7bTYu1cXLJ4Y4b9jt8s6Dds/9SvB0fgDM9lRi3vP0kFPxX9ypFX+6Ij+DjHiDCpCEIAnERMjQ0xNH0jyxLlcXr554FYAbJ91ocGv6Z7PYuC7vOgD+cXyIRTs8aUjqlKvptsX6qmnDlz4VMmdrN4UH/j6kXfzDU5nw2tlZ2K3B9ZF7m6fX8oUdJXQ6XMY2Jsi0O9p57eRrANw85WaDW9NXQkQCl47WUgn1z4dB2+OZGzb18xARHCNGXnrK1q714Bp8KvpHJ2opqmsnNsLG52Yat95if66elUW4zcKRyhb2S9GOs+ijYZeNuYz48OB6XeqjYv849g9c7iF8Xp54D6WxiG5rFGr+tT5u3TDlXwu2SG1+ZunOQT+92+nm1b3aKO/182Ru2GAE112BEOexyFOwY19pIx3dI+Om8cOSD2nubiYjOoOlmUuNbs45XTvhWhQUtlVso6S5ZHBPbq+Hg9r8MsOLdPTH21P4zKB7Cmtbu3j/SDUA1881YH2mC7h8Shqj4iOob+tm4+HhzQ0wm/eK36Pd2c7o2NEszFhodHPOcvX4qwHYcHoDDtcg14PrbocDnjmdRldt68/Uz0NUsrbO0rHBz4PTi3SsmZMddPNU4iPtXDktA4Dndwzys9LkOpwd3vX61uQF0Sitx8oxK4kPj6eirYKPyz8e/A486ffFScu0+cfBJCIO8q/Rvtc7aQbh/SPVNLQ7SIsNZ9mEFN+2zeQkEBMhIycpklHxEThcKnuKR8Y8Mf2itDp3NVaLMWvgDERmTCZLspYA8MrJVwb35P0vgLMTMqajZhk/Mfss09ZoZYarD0HF3kE99dW95TjdKjOy45mUEUQjfR42q4XrPEU79N5MoXn91OsAfG7c54JmnkpvizMXkxKZQmNXI5vLNg/uyQX/gu4WrWLamCDs4LGF93SADDI9sbKpk3cLtM6Pmw0uWX8uN3rWFHttXznt3SOv+NS5bCzaSKujleyYbOZnzDe6OWeJsEV4O0AGXbSjtRpOaOttFqYEQWGc/uhzog/+U+usGYSXdnsyP+ZkYQuyzI9gJ2dLhAxFUXqtJ2b+9MSmria2lGklnD877rMGt+bCrh6nXaDeOv3W4OY+7Pek/M2+Nbhy5nWRiTDFM1dBL24wAKqq8qKnSMcNQVzG95pZWiC26WgN9VLJDYCa9hpvmepgmqfSm81i46pxVwHwrxP/GtyT9dfxrH8DS5DeBug3hSffg7aBzwt+dW8ZLrdWoXRievB1foCW3TE6KYrWLicbDgy9IInZ6JV3r8u7DkswFGzqh56euLlsM7UdtQN/4sF/gurGnTmHtojgSpf1GrNU65zpbtE6awaotrWLD/TMjznBe60LVsH5ShfiHBbqBTtOmb9gx8aijTjcDvIS85iYONHo5lzQipwVRFgjKG4pHngBgbqTUL4bFKuWox6s9JvCAy+Co3NATzlU3syRyhbCrBaunhk8hR7OlJceS35mHE63yhsHKoxuTlDYcHoDbtXNrNRZ5MQFX0qpTu+d31y6mfrOAXZO1Z/yrNGlwKwv+q9xw5U6CTJmaPMzDw+8OMK/POv1XTsneN9zFovCTZ5Rsb9LeiIAp5tOs7t6NxbF4n1dB6Pc+FxmpMzArbp5u/DtgT/RM8dYnRZ8lY+9LJaea90gOh31zI+ZOQnkBWnnRzCTQEyElPljtRGxvSWNdDuHv8J9MNPTEj+bG/yjYaCttXJxtlbx8K3TA5zXccCT3jFuBcSk+adhvjBuBcRlQWcTHH1jQE/Ri3SszE8nPip41n7rz7WztZvWV6SkNoC3SMdV468yuCXnNyFxAlOTp+JUnbx5+s2BPWnPM9q/4y+F+CDvvdbXFDswsCJAJ6pbOVTejM2isHpakI46eKyZk41Fge2F9ZyqaTW6OYbTi84sy1pGenTwVJftj76w+4bTGwb2hLqTWpVSxYp7yjX+a5gvzPwioGidNfWnB/QU/Vp3fRBnfgQzCcRESBmfGk1ilJ0up5tD5eatOFXZVsnOKq1y0erc1Qa3ZuCuzL0SgLcKB5CeqKo9gdiM4KwI6WWxwixP5Tz9RvY8upwuXvHMuQrmtETdVTMzURTYVdRAcd3IXlPsWMMxjjYcxWaxccXYK4xuzgV9fvzngQFWT3S7YZ9nnSB9DlYwm7YGUKB4KzReeIkFfTTs4ompJEaH+blxw5MRH8HFE1MBeG3fyB6JVlXVuzi5vnByMLti7BVYFAv7a/ZT2lJ64SeESocjQEKO1k6Avc9ecPND5U0UVDRrmR8zgm95nVAggZgIKYqiMHeMNipm5oWd9Z62uelzGRUT3D27vV2UdRHR9mgq2irYV7Pv/BuX74G6E1rJ3MkhMOqnB2In34fm8984fXCkhsZ2B+lx4SzLSw1A44YnPS6CpeO1SlevjPCiHa+f1Ip0XJx1cdCVz+7P6tzV2Cw2CuoLONZw7Pwbl+6A5lIIi4VJIdDBE5cJYy/Svr/AqJiqqvzL89q9emZo3BB+znPj+saBcoNbYqzDdYcpay0j0hbJxVlBtI7kOaREpniLiegB5Dmpas886GDvcNR5i3b844KVgkMp8yNYSSAmQs68sYkA7Cw0cSB2SgvEQqFIR28RtgguybkEGMAFSr+xmnQlhIdAXnnSOMheAKgXnMj85kEtULtqRiZWSxAWIOnHNXp64t6yEbvQrMvt4o3TWuppsKcl6hIiElievRzoCSLP6fAr2r+TV4M9RBZcna4VR7hQIHagrInCunYi7BZWTg3u1Dbdyqnp2K0Kx6paOVbVYnRzDKNfK5ZnLycq2Mq6n4OeqXLB9MTyPVB/MnQ6HAEmfUZb6L3+FFQeOOdmLrfK6/u1a92aIJ6TGewkEBMhZ94YTyBWVG/KG8YTDSe8qVGrxqwyujmDpqcnvlP4zrkXvXS7vGuHhUwvIfSss3LolXNu0ulw8Z6nfPbqGaEzmnlFfjoRdgunato4UGbetN/z2VG1g+r2auLC4rzzHUOBflO4sWjjuT8T3e6e1+3UawLSLp+YejVYw7TlI6oPn3OzV/dqo0qXT0knOjy41g47l/hIOxd7Rszf2D8y0xNVVfUWvQiFVGDdZaMvw2axcbzhOMcbjp97Qz0tMVQ6HEFr54TLte/1zpt+7CpqoKali9gIGxdNCP7Mj2AlgZgIOdOy4gmzWqht7abIhPNZ9B62ZVnLQiI16kyLRy0mLiyOmo4adlfv7n+jwi3QWqmVhh9/WWAbOBxTtfk4FG+Flv7LTm85Xktrl5NR8RHMyk4IXNuGKTbCzuVTtJGEl0do0Q694MUVY68gzBrcc4x6uyjrIiKsEZS2lnK04Wj/G5XugJZyLS1xfJCuY9SfyETI0zqkLIde6ncTrWdeC8Q+Pyu0euY/6+mseeNAhSk7Fi9kf+1+KtoqiLJFcVHWRUY3Z8Diw+O97T1noZxQ7XCEns6aQ6+cMz1Rz/xYOTWdMJuEE0MlZ06EnAi7lenZWoCy04TzxDYWaYs+6iNLocZutXP5GK037ZwXqP2eXsKp14AtdG54ic+G7Plo6Ymv9bvJm54S8FdOG4UlRNISdfqaYhsOVOB2j6ybQqfbyQfFHwCh1TMPWsXSpVnawszvFL7T/0ahmJao86QnWg5pazGdadvpOqqau4iLsHHxxJRAt25Y9JvYE9WtHKsaedUT9Qq7K3JWEGELrdelPhL95uk3+w+iT38IrVWh1+EIWnqiNVxLq6w6dNav3W6Vtw5qnZHBXqE02EkgJkKSnp64q8hcCzufajxFYXMhdoudZVnLjG7OkH1m7GcAeLfoXZxuZ99fOrt7gphQ6yWEnlGxftITu5wuNh6uAmD19IwANso3LspLITrMSlVzF/tKG41uTkDtqd5DQ1cD8eHxzE2fa3RzBm3lmJXAOdITQzUtUTfxCgiLRWkqIbH95Fm/fs1TLXH19FGE26yBbt2wxEbYWT5RT08cWUU73Kqbd4q0jgP9mhFKlmcvJ9IWSWlrKQdq+5lLddgzl3jq50OrwxEumJ64t7SRiqZOYsJtXJQXWp0fwUYCMRGS5nnWEzNbwY73it8DYOGohcSExRjcmqGbnzGfhPAEGroa2Fu9t+8vCzdDVxPEpEPOIkPaNyx6IFb0MbRU9fnVxydqaelykh4XzpzRiQY0bngi7FZWTNbKK799qOoCW5uL/t5bkb0CmyU05hj1tjx7OXaLncLmQk42nhGshGpaos4eqQVjwKjGvunOTpfb2zN/VYhUSzzT5zzpia+PsPTEvdV7qW6vJsYe4x3RDSVR9ihW5KwA+sn+cLvhqKeQx5TQKPxzlt5zos94XeqZH5dOTiPCHlqdH8FGAjERkuZ6RsSOV7fS2N5tcGt8R78ZvGx0iKUxnMFmsXmLHWwq2dT3l0c8F6dJq8ESgh9BCaMhay6gwpG+6YkbDmg3hKGYlqi7Il8byXvncP9z4MxIVVXeLXoXwJtWG2piwmJYkrkEgI3FG/v+MpTTEnWeinMZTX0DsV1FDTS0O4iPtLMwN8mIlg3bZVO09MRTNW0cqRw51RP1Ih2Xjr40pOZk9nbFGK2D4IOSD/oG0WW7tLTE8DgYGzqFf/qY6ElPrDvep1COqqrea10oZn4EmxC8CxICkqLDGJcaDZhnPbHKtkoO1R1CQfH2soUy/W/oc4Hq3Us4OfgX7jynftITu51u3jmkB2Khe3G6ZFIqYVbtpvBE9ci4KTxUd4iq9ioibZEszlxsdHOGTA8i9aASCP20RN2Ey1EtdmK7KqC2p0qdngp82eQ0bNbQvKWJCbdxyaSRVT3R5XZ550OH2pzM3hZnLibcGk5ZaxnHG3tVT9Q76fJWhl5aoi4iDiZ4OoV7XesOljVT1thBpN3K8olBvkB1CAjNTy0h6F3G3hyB2PvF7wMwO202KZGhn3O9JHMJdoud4pZiTjef1h4s3wMtFVqKVG7ozoHrk57YWgPAJydrae50khIT7k2dDUWxEXaWTEgGRk56oj4SvSxrGeHWcINbM3SX5FyCTbFxrOEYRc1F2oOhnpaoi4hD9YwsWI5pnTmqqrKxQHuNhsraYefyWc/izhsOjIxA7EDtAWo6aoi1x7J4VOh2fkTZo1g0Skux14v9oKpQ4FnTL5Q7HKGn8+bwq96HNhzsSUuMDJO0xOGSQEyErHljtJvdXSaZJ6YHYpeODuGbpV6i7dEsyFgAwIclH2oPHvFcnPIuB1vo3vCSOBYyZ2sV3Dw9n296UjU+My09ZBZxPpdVU7URvbcPjYz0xFBPS9TFh8ezYJT2ntNHG7w985OuDN20RA91klZJVvGMqh+raqWorp0wm4WLJ4b2OkaXTErFblU4VdvGqRrzV0/8sFS7JizNWordaje4NcNzSc4lgJb9AUDtMa3aoDWsp+BFqJr0Ge3vqD0K1QWoquqdH/aZEM78CCYSiImQNXesNiK2r7SRbufZJY1DSWNnIzurdgKhPz+sNz090TtP7Mgb2r+h3ksIPaNiBa/hcvf0zJuhlO/KqekoCuwvbaK8scPo5viVWSqV6s5KTzymzcNh8mqDWuQ77jytsp6lfBc0V7DRM49x6fjkkFnE+VxiI+wszNVGot8/Um1wa/xPvyYsz1luaDt8YXnOchQULcW5raqnwzF3uZbeF8oi4rW/A+DYWxRUtFBY1064zcIlkyUt0RckEBMha1xKNEnRYXQ53RwoazK6OcPyYemHuFQXkxInkR2bbXRzfEYPxPbW7KW+bJfWq2axa3nzoW6SVjyAwo/Yf6qM+rZuYiNszA/RggG9pcaGM9dT9VGfg2NW7xZrAcuiUYtCulKp7tKcS7EoFg7VHaKy9FOtd95iC+20RF1sBvVR47Xvj27wvjZX5ZujZ/6yKdqN7bsF5n7PlbWWcaLxBFbFaorOj5TIFKanTgc8I33eDsfPGtgqH/JULOXY297X5rK8FGJCvPMjWIRcIPb73/+e3NxcIiIimDt3Llu2bDnntps2bUJRlLO+jhw50me7l156ialTpxIeHs7UqVN5+eWX/f1nCB9QFMVbPXFPcWinJ5qlWuKZMqIzmJI0BbfqZsv+J7QHc5dpvWyhLiUPEnPB1U3RDu3Cu3xiKvYQLRhwJr16otnTE82SlqhLjkxmRsoMADYf+Jv24Jgl5njPAZXxcwDoOvga+0qbUJSeACbUXTZZm+e2o7CBpnaHwa3xH300bFbaLOLDzfG61NMT3z/9plYxEUWrDGwGeiBWso0dh08AcOnk0J6TGUxC6o7hhRde4L777uMHP/gBe/bsYdmyZVx55ZUUFxef93lHjx6loqLC+5WXl+f93datW7npppu49dZb2bdvH7feeis33ngj27Zt8/efI3xg9ugEAPYUNxrajuHocHawtXwrYJ75Yb3pqSebKrdrD5ill1BRtPK+QMRp7Wb+UhOlauiB2LbT9TS0mWeJiN4q2yopqC/AolhMUalUpy8dsaVaS3fWX6dmUJGgLbZtK95CDO3MykkgLTa0577pRidHkZcWg8ut8uHxGqOb4zf6nGE9eDGDS3O0a/f26t20KQrkLIBYkwQrCaMhLR9UN8mVmwG4ZHJoz8kMJiEViP3617/mjjvu4M4772TKlCmsXbuWnJwc/vCHP5z3eWlpaWRkZHi/rNaeKi9r165l5cqVPPjgg0yePJkHH3yQyy67jLVr1/r5rxG+MDsn9EfEtlVso9PVSVZMFhMTJxrdHJ/Tb3A/pp0uBfP0EgJMXAXAnK7tWBQ3y0O8YEBvo5OjmJwRi8ut8sFRc85Z2VKmZVTMSJlBUkTop5Tq9EBsm7tVe8+ZKBBrjchETZ6AVXWywrIv5KslnumyKdrf855J0xNbu1vZUbUD0BYhN4vc+FzGxI3Bobr5KDLCPB2OOs+o2KWWPUwZFceo+EiDG2QeIZPg2d3dza5du/je977X5/FVq1bxySefnPe5s2fPprOzk6lTp/LDH/6QSy7p6YXZunUr999/f5/tr7jiivMGYl1dXXR1dXl/bm5uBsDhcOBwGJtOoB/f6HYEytSMKCwKlDd1UlLXQkacf3tG/XF+N5doPUxLRi3B6XT6bL/BIi82j1RbNDXONrZn5rMoMhXOcf5C7vWbuQCsUaS5GrkuvZa4cEtQt32w53fFxBSOVLbwfkEVV0031w0v9H3v+eL/LVhev7kxuaTZY6l2tLAjdRwL40af8z0XSvTz2j52FdF1J1hl3Ule3r8bfr59aXleEn/88CSbjlbT0dkV8LXR/P0a3lKyBafbyZjYMWRFZZnq/+7i9MX8rbmITdFRXDp+Vb/vuWD5jBgsZfzl2D76Ncst+zgyIT5o2x9M53egbQiZQKy2thaXy0V6et+bgfT0dCor+5/DMGrUKP785z8zd+5curq6+Nvf/sZll13Gpk2buPhircewsrJyUPsEePjhh3nooYfOevydd94hKipqsH+aX2zcuNHoJgTMqEgrZe0KT7z6AbOS1Qs/wQd8dX5VVWVjs7aviPIINmzY4JP9BpuFrS5ej4BXLJHUD+BvDKXXb4qaz1J2cEn3h2zYEBqjKgM9v2HNADY+KKjg9TdKCfGq/H04VSefNHk68U7BhmLfvfeC4fU7u83N22HwelgcdSb7XHm+NJk7gOXW/XywfRPHLCGV3HNebhWibVaaOpz8/u9vMcGgKVT+eg3/o+0fAGR3Z5vuepfZWAbAh1FRvPZpAVbl2Dm3DYbPiMFwudxcosaQqLSSW/4mGzacNrpJ5xUM57e9vX1A24VMIKZTlL53AqqqnvWYbtKkSUyaNMn78+LFiykpKeGXv/ylNxAb7D4BHnzwQR544AHvz83NzeTk5LBq1Sri4owtVepwONi4cSMrV67Ebg/ttTkG6lPnYZ7bUYoldRyrPzPpwk8YBl+f31NNp2h8o5EwSxh3f/ZuIm0mHO53dRP7+3t5PSKWw7Hw/1afOzUx1F6/nQ4XD+/6hKWWHVwSdRL7ef62YDDY8+t0uVl3chMtnU6yZyxhVk6C/xsZINsqt9H9fjcpESl85aqvYFGGfzMfNK9ft4vYP/4Hb4eFsy9W4adXXnnea1qo0M9vQcJymit/RbzSxudmZ6BmzTO6aT71YccBXtlXQXvieL9f087kz9ewy+3ily//EoDbL7qduelzfbp/o6lvvMuf6100WK1kzc9iTtqcs7YJms+IQdp2up4P9sziOutHXJvdjHp5cF7rgun86tlyFxIygVhKSgpWq/Wskarq6uqzRrTOZ9GiRTz99NPenzMyMga9z/DwcMLDz16M1m63G/4frwumtvjb3LHJPLejlP1lzQH7m311fj+t+hSA+RnziYsM8fVGzqVsGwtbGrElxVDaUU1lRyU5cTnnfUqovH4/OtXAO90z+UUERNXuh846iA3+UtoDPb92u1ameMOBSj462cD8ceaZA/dppfbeW5q1lPAw3y4ubvjrt2QvixqrscdnU9pZR2lHKePixxnXHh9SVdhyqokt7ml81rod2+lNMHax0c3yqcvzM3hlXwUfHKvlR1dNM6QN/ngNH6w+SGNXI3FhcczLnIfNEjK3oBemqnD6AxaFdfJmTDTbqraxMGvhOTc3/DNikLacqKfMNZvrrB9hO7kRrvy50U06r2A4vwM9fsiM54eFhTF37tyzhhs3btzIkiVLBryfPXv2MGpUz4KrixcvPmuf77zzzqD2KYw1x1M5cX9pEw5XaC3srBcLuCjrIoNb4kcn3iVaVZlp0wLNT8rPP6czlHxwpJpa4imJnKI9cPwdYxvkBysmapUgNx0zVxW3j8o+AuCibBO+9469RZSqMs+q5bVtKT33Mi+hpqIdqlq6+ESZpT1w4l1D2+MPF09MxWZROFXTxunaNqOb4zN6tcSLsi4yVxAG2np9TSVc1KXN8/64/GODG+Rb7x+pZrN7Bm7Fqq0HWn/K6CaZRsgEYgAPPPAAf/3rX3niiScoKCjg/vvvp7i4mLvvvhvQUgZvu+027/Zr167llVde4fjx4xw6dIgHH3yQl156iW9961vebe69917eeecdHnnkEY4cOcIjjzzCu+++y3333RfoP08MUW5KNPGRdrqcbgoqBjYUHAzaHe3sqtoFwLLs0F/U8pw8N0pL07T0IbNcoFRV5b0CrZpg9zjPAtXH3jawRf6xfJI2Cra/tJG61q4LbB0aylvLOdl0EqtiZfEoc42mAHDsLQAuHrUIMFcgdqRJS7Fs00ufl+2CtjoDW+R7cRF2Fo7T5pu+f8Q8FUv1TjhTXu8817klKTMBKKgroL6z3sgW+UxJfTvHq1tps8TgztY+Uzhmvk5Ho4RUIHbTTTexdu1afvrTnzJr1iw2b97Mhg0bGDNmDAAVFRV91hTr7u7mO9/5DjNmzGDZsmV89NFHvPHGG1x33XXebZYsWcLzzz/PunXrmDFjBuvXr+eFF15g4cJzDymL4KIoSkiuJ/Zpxac43U5yYnMYEzfG6Ob4R0slVB4AFJZMuQmA7ZXbcbiNr2g0XMerWylr7CDcZiFr4bXagyc/AKc5ghVdelwEU0bFaSlhx2uNbo5P6KNhM1NnmmZBWa/mCqg6CChcPF3rmNxVtYvW7lZj2+UjBY1aIDZz6hRtbSNUOPWBsY3yg4vztA6Qj0yynlhtRy0F9QUA5uz8OK5lVqXkXcmkxEmoqN71QUOdvnzJ3NGJ2KZcqT3o6ewRwxdSgRjAN77xDQoLC+nq6mLXrl19im6sX7+eTZs2eX/+7ne/y4kTJ+jo6KC+vp4tW7awup/J9Ndffz1Hjhyhu7ubgoKCPoGaCA2huJ6YNzXKzGmJJ9/X/s2cxZTsxSSEJ9DmaGN/zX5j2+UDH3h6qheNSyYiZzbEZICjDYrMk3qp09dH22SS9cT0ESJT9szrQUnmbEanz2RM3BicqpOtFaF/U9je7eRksxaIXTwxFSZcpv3ChOmJyzyB2Ken6ulyugxuzfB9WqHNyZySNIXkyGSDW+Nj3W1Q5Mn0mHA5S7OWAvBxmTmyP/RR2Usmp8GEy7UHi7eCo8PAVplHyAViQvRnzpgEAPaUNBrajoFSVdU7P2xZlglvBnX6DdKEy7EoFm9PqBnmiX10QhsdunhiKigKjL9U+4UJe+dXeNITNx+vxe0OzBIR/tLt6mZb5TbApJ0geueH5/Wof76YIT1x2+kGXKpCdkIE41Kie24KT7wH7tCaH3whkzNiSYkJp8PhYndRo9HNGTZ9dGhxpglHwwo/Alc3JIyGlDyWZmqB2Cfln+BWQ/t12eV08ekpLfV3xaRUSJ0MsaPA2akFY2LYJBATpjAzJwFFgaK6dmpDYB7LycaTVLZVEm4NZ37GfKOb4x9uV89NoeeGaUmWVgTnk7LQDsQ6HS62n9by/5flpWgPjvfMWTlpvkBs7phEYsNt1Ld1c6CsyejmDMvOqp10ODtIjUxlUmJgS4P7ndsNpzZp33tej3qwubViK6oa2kG0nhq7LC9FK8c/ehHYo6GtGqoOGNw637JYFO9ny5YQT09UVdXb+bYk04SF0Hp1OKIozE6bTaQtkrrOOo7WHzW2bcO0q6iBToeblJhwJmfEap2O48x7rTOCBGLCFOIi7ExIjQFgbwjME9NHw+ZnzCfCFmFwa/ykfA90NEB4PHjW+dEvwofqDtHQGTpppGfaWdhAl9NNelw4eWna645xK7R/K/dDmznmUunsVgtLJ2g3hZuOhvZNYe+UYDOsrdVH9SFoq9GCk+wFAMxOm43dYqeyrZLiluIL7CC4bT6u9cwv87wWsYXDuOXa96ZMT9QDsdD+PDneeJzajloibZHMTpttdHN8zxuIaUWb7FY7CzO0OgOhXpzqI89r76IJyT2fl3qnowmzP4wggZgwDW/BjpLgv8HXc8dNmRql0y9O41eAVStVnBaVxoSECaiobKvYZlzbhklPS1w6IaXn4hSTBumeNX/0UQkT0dMTNx0L7XlieoqUPjprKvoI9NiLwBYGQJQ9illpswBCunhAUV0bRfXtWBSVRZ6KgkCveWLvGdMwP7rIE3AeLG8K6Yql+utuXvo8wqxhBrfGx+pOaqXcLXbI7Zlm4M3+CPE0/I8917qL8nqtIentdDwAraHdMRcMJBATpjFntF6wo9HYhlxAh7OD3dW7AZOmaeh6p2v0oufPh3JP4UcntIuPNy1RZ+L0xIs9BTv2lTTS3BmaVS9rO2o50XgCwNtjbSr6605/HXroczP1ggmhaLNnHbtxsRAb0WsNqvGeQKz4U+gM7bTZM6XFRTA5IxZVhY9Phm6JfnOnJXo6AEYvgvBY78MXZWqdrHuq9tDmCM214Brbu9nvSUXXOwWAvp2Opz80oGXmIoGYMI1ZnhGxfSWNuIK4oMCe6j043A7So9IZGzfW6Ob4R0eDtr4P9NwoeegX40/KPgnJOSv1bd0cKtfWq1s64YxAbFyvlI0Q/NvOJzMhktyUaNwqbDsVmuvj6KOwk5MmkxiRaHBrfMzR0VOxUy8c47HIs57Y9ortuNyhWYHvQ08gNjnhjOIHSbmQPAFUF5wO/YIkZ/KmJ4boguqdzk7vepmmDMT07IcJfa9zOXE55MTm4FSdbK/YHvh2+cDWk3WoKkxIiyEj/owpFCbudAw0CcSEaeSlxRIVZqWt28XJmuBdM0fvlV40apH55qjoCj8G1Q0pEyE+q8+v5qTPIdwaTnVHNaeaThnUwKH7+EQtqqpVNUuLPePiNGYJWMOhuQxqjxvTQD9aMl4rO62nq4QaPRDTAxNTKd4Kri6IzdTed71MTZ5KbFgsLY4WDtcdNqiBQ+dwudnqGRGaHN9PB4eeKnV6c+AaFSB6GfuPTtSGZMfV7urddLm6SI9KJzc+1+jm+JbLqVVMBMhdftavQz37Y4uelnhmhyOYutMx0CQQE6ZhtShMz9IWZ90XxGXsPy33BGKZJrwZ1OnpCrkXn/WrCFsEs1JnAdrizqGmZ/JyPxcne6SWogKmnMisjwB+cjL0AjFVVb2dIAtHmTEtUS9bf4lW2awXq8XqTcUMxfXE9pc20dbtIiHSTlZ0PxvonzMmTJNakJtEmM1CRVNnUHcwnot3TmbmEvN1PFbug64miIiHUTPP+rVeqj9U50Of91pn8k7HQJJATJjKrJwEAPaVNhrajnNp6GzgSP0RwKS98jq9Z7qfXkKABaO0im47KncEqkU+oaqqt1DHRWfOD9OZOGVj8bhkFAWOVbVS3dJpdHMGpaSlhIq2CmwWG3PS5hjdHN87uUn794y0RJ3+eROK88S2egL/hbmJWPq7lx+7DFCg5gi0VAW0bf4WYbeyMFcrTrL5WOh1gOijQeZMS/QE/mOXgcV61q/nZczDolgobC6kqi20XpfFde0U17djsygsGt/PAty9Ox31TiAxJBKICVOZqQdiJcE5aXt75XZUVCYkTCAl8hw38qGupUq7IULRqrf1Y0FGTyAWSgtenq5to6yxgzCrhQW5Sf1vpKdsFH4ErtAsanEuidFhTB0VB+BNFQsVegAyM3UmUfYog1vjY6291tE6R+eHPgK/t3ov7Y72QLXMJz7xvNYWjzvHey4qCTKma98XmnieWIitJ1bbUcvxhuMoKOYchT5P5gdAXFgcU5KmAKGX/aF3OM4enUBMuK3/jaSMvU9IICZMRQ/ECiqa6XQE36T03vPDTEu/EcqYrt0g9SM/JZ9IWySNXY0cbwidtAZ9btScMQlEhZ3j4pQxA6KSobsFSncGsHWBoacnhto8MT09yJQ3hHrBgIwZEJPa7yajY0eTGZ2Jw+3wVm0NBZ0OFzuLtCVJFo3rp2dep98Mm3DpiIsmaP+nn56qx+EKnY4rvUiFKYvjOLu0Sp1wzs4P6Mn+CLVA7ONeS7Sckz76bsJOx0CSQEyYSmZ8BCkxYTjdKocrmo1uzln0+WF67rgpXaCXEMBusTMnXUsPC6ULlL6w6rK8/m92AbBYei7MJuwp7CnYURcyxQPcqtv7OtNLuZuKniKlF63oh6Io3lEx/XMoFOwubqDb6SYtNpxxKecZyTRxwY7JGbEkRtnpcLjYH6Rp9/3R33N6BoSplGwHZyfEpEPqpHNups/N3FaxLWQ+L11ulY9P6te68wRi6dMhKgW6W6E0tKYZBBMJxISpKIrCzOwEIPgKdpS0lFDaWopNsTE3fa7RzfEf/abwPL2E0HNxDpVAzOVW2XpKS5Hqd/Jyb96UjU3+bZQBFuQmYbcqlDV2UFwfGiluR+uP0tjVSJQtivyUfKOb43v6KPQF3nOhOE9MT4Hts3h6f0YvBosNGougoTAwjQsQi0VhYa7WAfJpCC0doc8B1keFTMU7D/ris4rj9DY7bTY2xUZFWwWlraUBatzwHCpvorHdQUy4jRme+6l+WSy9CuWYLyU4UCQQE6bTM0+s0dB2nEm/+ZmROoNoe3+lv0ygoVC7EbLYYMz5Rx70QGxX5a6QWNvocHkzLZ1OYiNsTPNU5zwn/Ya4bBd0h+ZinucSFWZjdo6WZvTxidCYJ6a/9+ZlzMNusRvcGh9rLNbec4oVRp8/7VJPyzzacJS6jtD4v/POD+uvYEBv4TGQNU/73oSjYvrfHypzMyvbKiluKcaiWJidNtvo5vje6YF1OEbZo5iROgMgZNYT099zi8YlYbdeIEzQ54GbcG5moEggJkzHG4iVBlfBDm/ZejPPD9N7xbLmQnjseTednDSZWLu2ttGRhiMBaNzwfOoZDVuYm4S139JtvSSOgfjR4HZCSWiWLj6fJRM86YkhUsbe1OuH6esYZc254HsuKSKJiYnaGmM7q4J//mJrl9PbobbkQoEY9OqdN28gtrOoni5n8Hdc6aNhU5O0NexMpatF62QDGHf+QAxgfsZ8IHSyP/Rr3XnnZOrGLtP+Ld0BjtCqpBssJBATpjMzWxutOF3bRmN7t8Gt0bhVN9sqPTeDpl4/rFe6xgXYLD0pmqHQUzioixP06in8yE8tMo4+gXvryTrc7uCe99Dt6vYWpzBloQ799XWOCqVn0m8Kd1YGfyC2o7Aep1tldFIU2YkDqHTZOxALkfk4A5WXFkNydBidDjf7g6yTsT960DF/1HyDW+IHRVu1TrbEsZAw+oKb65872yu3B/08MafLzc7CARTH0aXkafPknJ09wakYFAnEhOkkRIUxNlm7aAfLBetI/RGaupqIskUxLWWa0c3xD1UdVCAGodNT6HKrbD+tzc2QQAxmZicQFWalvq2bI5UtRjfnvA7UHqDD2UFSRBJ5CXlGN8f39FFovWf6Auala+l7oTAipqfhDWg0DCBnAdgioLUKao76sWWBpyiK97MnFNITvfPDzFioY4BpiboZqTMIt4ZT21HL6ebTfmzY8B0qb6a1y0lchI0pnqVKzktRTH2tCwQJxIQpBds8Mf2iNDd9rvnmqOhqj0FrJVjDIXtgF1+9p3B31W4c7uAtf3u4vJmWLm1+2IAuTtBzcSrbBV2t/mucAcJsPeuofRLk6Yn6yM+89HnnL/YQihqKoKlYm5OZM7DRPn0U+kTjCeo7g7vwg/7auuD8MJ0tvGeRWROmJy4KkXliZa1llLWWYVNMunj6qQtXBu4t3BrOrLRZAOyoCu7qgnrmx4Lc5Aun4OtkntiwSCAmTMlbOTFISv3qN4Om7B3U6Tc+oxeCPWJAT8lLzCMhPIF2ZzuH6w77sXHDM6j5YTqTzxPTe+f1kcJgpY/8zMuYZ3BL/EDvgc6coxWrGIDEiETyErWRwV1VwZtK1NjezaFybQmSAQdi0DNKoY9amIi+oPXu4oagXCdTp3c85qfkm2/x9Pb6XounDywQg54y9qESiC061+Lp/dFH40u2yzyxIZBATJiSPiK2t6TJ8Jxsl9vlveHRU/FMyVtCe+AXJ4ti8aZKBfM8sUHPD9OZOGVDHxHbXlgftPPEHG4H+2r2AT0peabifc8NLC1Rp58L/YY5GH16qg5V1eZGpcUOrGMH6Pn8KfoY3KGz+PFAjE+NITU2nC6nm71Bku3RH/11ZcrrXfFW7d/UyRCTNuCn6SX8d1btxK0G5+ty0PPDdMkTtHliri4oC/6U52AjgZgwpfzMOGwWhdrWLsqbjO2hOdpwlBZHCzH2GCYlnXvhx5CmqlD0ifb9mIEVDdB5iwcE6ZyVIc0P05k4EJueFU+k3Upju4Pj1cGZenmo9hAdzg4SwhMYnzDe6Ob4lqoOulCHTn/PBXcgNsT33KiZYI+GjgaoCf5qrIPRe56Y3jkUbFRV7SnUYcZArPBj7d8xSwb1tPzkfKLt0TR3N1PpqvRDw4bvcMUQUvDBM0/M0xlkwmudv0kgJkwpwm5l8iitZK7R88T0m5056XOwWWyGtsVv6k5AW402PyxrcHMC9Dkre6v34nQ7/dG6YTlU3jS0ixP03CCX7zbdPDG71cLcMdp6YttOB+dNoR7cz0mbg0Ux2eWusQiaSsBiH/D8MF3veWINnQ3+aN2w6Z0fCweTIgVgtWtFO0AbFTMZPWUsWOeJlbaUUtlWic1iM+f6YfpraszSQT3NZrF554kVuYp83CjfGFIKvk6/1snCzoNmsiuTED30FeGNrpyozw+bn27C3kGdfnHKnqdNmB+EvMQ8YsNiaXe2c6zhmB8aNzzDujiZfJ7YQk964rYgnSempwSbcn5Y7zX7wga3QHxSRBITEiYAwTlPrKnDQUGlNj9swdhBBmLQc5NswkBssWdEbE9xY1DOE9NHw2akzCDSFmlwa3yssxkq92vfD3JEDHpSggudhT5slO9sG+ooNMh6YsMggZgwrRlZ2npiB8oaDWvDiJkfVuTJmx/CxcmiWLw9p7trdvuyVT4x5BQpnYnTE/V5YttO1Rs+F/NMTreTPdV7ALPODxtaWqJOHxULxpTgXUX1qCrkpkSTFjeI+WE6/XOo6BPTrSeWmxJNelw43S43u4uDbzRTL0ZhyutdyXZQ3ZCYC3GZg366/p4rdBYG3edl7xT8hblDuNYlj4eYDG2eWGnwpjwHIwnEhGlN0wOxUuMKdoyI+WHQa37Y4AMx6LlA6TfOwcLpcrNjqPPDdCYOxGbmJBBms1Db2sXp2jajm9PH0fqjtDnaiLXHMjFxotHN8a1hzA/TBfM8se2ntQBjSKNhoI0SWsO09cTqT/mwZcbrO08s+EaiTT0KXeR5zw0yLVGXn5xPmCWMNrWNwuZC37XLB7xLtITbmJo5yBR8kPXEhkECMWFaE9NjCbNZaO50UlzfbkgbRsT8sMZibS0jxTrg9cPOpK81s7dmb1D1FA558nJvJp4nFmG3MstToTTY0hO988PS52C1WA1ujY81FEJz6ZDmh+n0zo9jDcdo7Gz0Xdt8YLtnzuH83CEGYvYIyPIEAiZMT9RHoncE2XuuvLVcmx+m2JiRMsPo5vjeMDscw6xhTE+ZDsCemuDqdOxZP2wIKfg6vXqrrCc2KBKICdMKs1mYkqEV7DBqntjImB/mSUvMnDXgtYzOlJ+cT4Q1goauBmrdwbNA8LDmh+kSx0CC+eeJBdt6Yvp7Tw84TKX4U+3fzNkQNrR1mlIiUxgXPw6AXdXBM0+so9vl/bxeONRADGCsPk/sEx+0KrjM94wU7ilpwOEKnlLo+mjYlOQp5ls/rLsdyjyp80MMxKCn03F3dXCl4esFl4ac+QE9FZNLd4KzywetGhkkEBOmNj1bS088WBb4QGzkzA8bWjnf3uxWO9NTtZ7CYJrIvMOzpsqC4dwQQk8qi74GjYno8wm2naoLmtFMt+r2BhemnB9W7AkuRi8a1m70c6MHrcFgT3EDTrfKqPgIshOHUezBO0/MfCNiE1JjSIiy0+lwG3JtOxc9uNCDDVMp2wluB8RmQuLYIe9mdqpnPnQQBWLu3vPDBlultLfk8RCdqs0TKw+uEb9gJoGYMLXpnnliRoyIjZj5YXpwMXrogRj0XLyDJRBzu1V2FmoXp/lDnaui02+Y9ZEME5kzJgGbRaG8qZPShg6jmwPA8YbjtHS3EGWLYkryFKOb43v662gYnR/QM48nmCon6imuC3KTUJQhjkKDliatWLXU6cYSH7UuOFgsCvPGeNITC4NnJHp3lRZczE43Ydl6ff2wsUu1+VBDNCNlBhYsVLZXUt5a7qPGDc+x6haaO51Eh1mZOtQUfNDOi/daZ75OR3+RQEyY2vSsBAAOljfhdge2t35EzA9rrYFaT8n5YfbO6ylkRc7gWGPlVG0rDe0OIuwW8jPjh7ez0Yu1f0t3grN7+I0LIlFhNu/Ic7DME9Pnh81Om22+915bbc97bojzw3R6tdKjDVphk2CwvVcgNizhMVq6NJgyPXFBrraGn17YxGgNnQ2catIKo5hyRMwHmR8AUfYoMq1axcVg6QDR5xrOGZOIzTrMsEC/1pmw09FfJBATppaXHkO4zUJLp5OiABfsGBHzw/QUqbR8iBrejdPM1JlYFSuNaiMVbRU+aNzw6GmJszyVAYclZSJEJoGzAyr2+aB1wWWBd55YcCwyq7/3TFm5Te9pTp0y7PdcRnQGWTFZuFU3+2qMf112O3tKsg+5YmJvJk5P1EfpdxbVB7yTsT96qt24+HEkRiQa3Bofc3b3lGQfYsXE3sbaxgJBFIh5rnX6KOuw9A7E3MEzfzGYSSAmTM1utXir3R0IYC69W3V7L0ymvBnUDbOKVG9R9igmJ04GgqOMvd5LOOy0RPCkbOgXKPOlbCzS54kFwYiYqqrmnqviTUtc7JPd6aNiwfCeO1DWSJfTTVJ0GBPShlb4p48x5i3YMS0rngi7hcZ2BydrjK/Gqqclzkk34XuufA84OyEqRetUG6YxtjFA8MwT60nB90EAnTED7NHQ2Qg1R4a/vxFAAjFhejOy9fXEGgN2zBONJ2jubibSFsnkpMkBO27A+ShdQ+e9KQyC0r47inwYiEHPjbMJA7G5YxNRFCiqa6e6pdPQtpS0lFDfWY/dYic/Jd/QtviFHlSM9nEgVmX8e27b6Z4bwmHND9ONXgQoUHccWquHv78gYrdamJ3jSU8MgnlieiBvys6P3tc5H7wux1i1QOx002nqOozNIihr7KC8qROrRWHW6ITh79Bqg2xP53Ox+TpA/EECMWF60wwo2KH3Ds5MnWm+OSq6ziaoPKh976tALDU4ArHKpk5K6juwKDDbFxcnMHXKRlyEnUnp2lIRuwqNnbOi3xDmJ+cTbg03tC0+193Wk9rqo0BMv3HeX7sfh9vhk30O1Q7v/LBhlNDuLTIR0j3BuBnTE4NkPbF2RzsFdQWAWZeL8HSe+eg6F2WJYkLCBMD4UTF9NGxaZhxRYT66V9HPk8wTGxAJxITp6SNih8qbA5ZLb+o0DV3JDkCFxFyIzfDJLmelzgLgVNMpGjqNu6Hf6RkNmzIqjtgIu292Omom2CKho17roTeZeZ60lp1FwRGImbJyW+kOUF0QnwMJOT7Z5biEccSFxdHh7OBo/VGf7HMoXG6VnYU+nB+m81ZxM98afvp52mFw58f+2v04VScZ0RlkxmQa2hafc7t71n8cZnGc3uaketYTqzI2ENOrbs7zy3tOArGBkEBMmN6E1Bgi7BZau5ycrvN/ZTBVVb1rGM1NM2HvoE6/OA2zWmJviRGJpFpSAQwtHuDT+WE6q70nZcOEc1b0id5BE4ilmjAQ029sfPiesygWb3qikTeFx6paaOnSSmhPGRXrux3neM6VCRdTnz06AatFoayxg7JG45aO8HY8mjEtsfaolv1hj4KM6T7brf6eM7pgh9754ZP5YbqsedrSEU0lpls6wh8kEBOmZ7NavGtjBGLxy9LWUqrbq7FZbN5Fik2pxHNTmLPAp7sdbRsNwN7qvT7d72Ds8F6cfBiIgalL+84do13ID5U10dHtMqQNjZ2N3hLas9JmGdIGv/Lx/DBdMBTs0AP4WaMThl9Cu7fRnlGMyv3QHdjKuf4WHW5jWqZ2bdtp4DwxPRAzZ1qi57M6a67WmeYjevbHsYZjtDuMeV02tTs4WtUCwFxfVEzUhcdoGSBgymudr0kgJkaEGdkJQGDmiekXpanJU4m0Rfr9eIZwOaHU05OX47veeYDRVi0QM+qmsLnTwZHKZqAn3c5nTFywIzsxkvS4cJxulb0ljYa0YW/NXgBy43PNV0Lb5dDWoQOfB2J6CvXu6t2oqjGl0Hd5Agmf3hCClsYZmwluJ5QFR7lwX9I7i7YbNE/M4Xawv3Y/0BPQm4ofMj8A0qPSGRU9Cpfq4kDtAZ/ue6B2FzegqpCbEk1qrI/n03o7Hc2X/eFrEoiJEUEv2BGIEvb65FtT9g7qqg6Cow3C4yHVt1Uh9RGxQ3WHcLgCXzxgd1EDbhVGJ0WRHhfh251nzwfFAo1F0Fzu230bTFEU7zyDXUXG3BTq7z1T3hBW7tfecxEJPn/P5SfnE2YJo76znuKWYp/ue6B2FetrGfk4gFaUnlGxEvP1zs/zzhMz5j13pO4IHc4O4sLiGJ8w3pA2+JU+ouPjDkfoGRUzqtPROz/M1+856NXpaL73nK9JICZGBG/BjrImvxfs8KZpmHp+2Hbt35z5YPHtx0iKJYWE8AS6XF0cqQ/8OiQ7/ZWWCBAeq62zAqYcFdMv6EbNE9PTWU0ZiBV5Xi+jF/v8PRdmDWNayjTAmHli1c1alVLFl1VKe8sxb8EOfW7PsapWmtoD33Glj0LPTpuNRTHZLWVrNTScBhTtWudjevq0fg4Dza/XOv09V30Y2o1fXiGYmexdI0T/xqfGEGm30tbt4lSt/wp21HbUUthciIJizjkqOu/8MN9VkdIpisKMFC1YMeICtcOXi1v2R0/ZKDJjIKZd0HcXNQSsQqmuy9XFwVptOQVTBmIlvi/U0ZuR88T0wH1SeqzvqpT2ps9jLd1uuqUjkmPCGZcSDcDuksB3gOidH6a83umjOWlTISLe57vX33P7q/fjVgP7uuxyutjrWVvV5yn4ADGpkKyV6DdjoRxfkkBMjAhWi0K+Z1LzgbJGvx1Hv4mZkDiB+HDff3AHDe+ImO8DMYCZKdpE30AX7Oh2ur3zm3xazrc3E5f2nTIqlqgwK82dTo5Xtwb02IfrDuNwO0iKSGJ07OiAHtvvVNXv7zl9npghgZinZ94vN4SgVbuzR2nV72oCP8rub3M8I9G7AzwSraqq9zN6ZurMgB47ILzzw/zznstLzCPSFkmLo4UTjSf8coxzOVDaRLfTTUpMGLmeQN7n9Gud/tkl+iWBmBgxArGws6nL+OqayrSytIpVqyTlBzNSPSNi1XsDWjzgcEUzXU43CVF2xqf6+eJUfQi6WvxzDIPYrBZm5SQAPWuxBYq3bH3abBRFCeix/a6xCFqrwGKHzFl+OYR+I13YXEhdR51fjnEuPfPD/NT5YbX3fFaZcJ6YXrF0V4ADsYq2Cqo7qrEqVm9qq6n4cX4YgM1i63OtCyS9MvDcMYn++7zUO40kEDsvCcTEiKHPE/NnCXt9TRBTF+rQb2Qypmllav1gatJUbIqN6o5qKtoq/HKM/ug3MnNH+/HiFJsBCaNBdZuyips+krgzwIvM7qnqCcRMp2SH9u+omWD3TyXW+PB4JiRoqUSBTAnu6HZxyPOZPNcfRQN0Ju6dnzNaO297SxpxugKX4qYHD5OTJpuvQrCjAyo8a1n6aUQMegp2BDoQ2+3vzg+AbE9KcNkureqr6JcEYmLEmJ6lB2LNuPwwf6W1u5WjDUcBk4+I+TlFCiDSFsmU5ClAYC9QemrPHH/eEELPBcqEN4U9BTsCNyLmVt19igaYjp4i5cf3HPSMigVyMfV9pY043SppseFkJ/rxZj7HvCnBeWkxxIbbaO92caQycKPs+nvOlPPDynaD2wExGZAwxm+H0T+vAtn5oapqr2tdgv8OlDJRm1vn7NAqLYt+SSAmRoxxqTFEhVnpcLg4VeP7+Sv7avbhVt1kxWSRHp3u8/0HjWL/FeroTb8pDOQFSu8l1HuY/cbEKRuzRydgUaCkvoPq5s6AHLOwqZDGrkYirBFMSZoSkGMGlDcQ833ltt68gVh14AIxfRR63lg/jkKD59wpWhW81mr/HccAFovCbH2eWHHgRqK9hTo8ozqm4i2Os1BbAsFPZqTOQEGhpKWE2o5avx2nt+L6duraugmzWsjP9ONcdovF1J2OviKBmBgxrBaFaZn+myemz1Ex9WhYdxtUehaf9HMg5i3tG6ARsfLGDiqaOrFaFGbm+LnQiomruMVG2JmUoRXGCVQZez1Yz0/Jx271Q9U9I3W1QtUh7Xv9psZP9PdcINfw0wMxv3d+RMRr1e/AlKNic0cHdp5Yu6OdYw3HAJOOiOlLHfhpfpguNiyWCYlaSnCgOkD0YD0/K44Iu9W/B9OvdVI58ZwkEBMjij8XdjZ1GV9d2S5QXRCXBQk5fj2U3jt/rOEY7Y52vx4Lei5OWuU/m38Plj6tp4pb7TH/HssA8wJcPEBPpTNl5bby3dp7Lj4H4rP8eqixcWOJD4+ny9XlTbP2J7db7TUi5se5KjoT3xTODfCI2MHag7hUF+lR6WREZwTkmAHjdvu9YmJvs1MDu3REwDo/oNd7bof/jxWiJBATI4pesMPXgZjD7WB/7X7ApHNUdN4UKf/2zANkRGcwKnoULtXFgdoDfj9e70Idfme19VRxKzVfyoY+70BfCsDf9tdo7z1TBmIBfM/1XsMvEPPETtW20tThIMJu8S4v4lcmXjpiZk58QFOCTT0/rO4EdDaCLRIyZvj9cPo53FMTmEBsd1Ej4OfiOLqsuaBYoKkYmsv9f7wQFHKB2O9//3tyc3OJiIhg7ty5bNmy5Zzb/vOf/2TlypWkpqYSFxfH4sWLefvtt/tss379ehRFOeurszMwcxtEYOkjYofKm3xaXepY/TE6nB3EhsUyPmG8z/YbdAJQqKO3QKYnBqxQhy7bM9/HhL3zs3O0c3igTFurxp9auls42XgSMGsg5nnP+TktUeedmxmA95xeWXNmdgJ2awBuR/TPrcr94Ozy//ECqHdKcCBGxUw9P6zUM3qTNUdb+sDP9Ovc4brDdLn8+7ps7XJypLIZCNCIWHgspOVr38s8sX6FVCD2wgsvcN999/GDH/yAPXv2sGzZMq688kqKi4v73X7z5s2sXLmSDRs2sGvXLi655BKuuuoq9uzp2+sQFxdHRUVFn6+IiIhA/EkiwMalRBMdZqXT4eZkTZvP9qunFMxMnYlFCam31cCpas8FKkA3hfpF3t89hZ0OF4fKA3hxAlMX7BiTHEVilJ1up5uCima/HutAzQFUVLJjskmOTPbrsQLO7e55zwVgRAx6bgoDMSKmBwwB6ZkHSBwLUcng6oaK/YE5ZgDN9YxE+zsl2K26va8PU46IeQOxwCxDkx2TTUpkCk63k0O1h/x6rP0ljbhVyEqIJCM+QPe53jnRkp7Yn5C6Y/z1r3/NHXfcwZ133smUKVNYu3YtOTk5/OEPf+h3+7Vr1/Ld736X+fPnk5eXxy9+8Qvy8vJ47bXX+mynKAoZGRl9voQ5WSwK+d6FnRt9tt/ei8maVv0p6GgAazhkTA/IIfXe+YO1B/26sPP+0qbAlNDuTR8Rqz0G7YFd/NjfFEXxLuy8x8+98975YWkmHA2rO6G952yRAXvPTU+ZjkWxUNFWQVVblV+Ptbu4EQhg54ei9LzvTHhTGKiFnQubCmnubibCGsGkpEl+PZYhSndq/2b7t0qpTlEU77VOT7P2F73zY/boBL8epw9vp6P5sj98wc8z0n2nu7ubXbt28b3vfa/P46tWreKTTz4Z0D7cbjctLS0kJfWdFNza2sqYMWNwuVzMmjWLn/3sZ8yefe4b6q6uLrq6eoaPm5u1Hl+Hw4HDYeyidfrxjW5HMJs2Kpbtp+vZX9LANTMHF3T3d35VVfUGYtOTppv23CtFn2ID3KNm4lIV8MPfeeb5zY3JJcwSRlNXE6fqTzE6brTPjwmw47RWNnhWTjxOp9MvxzhLWBy2pPEo9SdxFn2KOmGl3w8ZyM+HGVlxfHC0hl1F9dyyINtvx9Hfe9OSphn+3vP1+VUKP9Hec5mzcbnR1jXyMzt2JsRP4FjjMXZX7uby0Zf75TjNHQ5OVGvLiEwbFT2gc+aL82sZNQfrsbdwl2zHNe+uIe8nGM3IjAW0lODW9k7Ch1ARbyDneFelthD91OSp4CJgFTYDorsVW/UhFMCRMcvn17lznd/8pHzeK36PfdX7/Po5tqNQ6/SblR0XuM/LUbOxA2r5XpwdLWDz30hcMN0DD7QNIROI1dbW4nK5SE/vuz5Teno6lZWVA9rHr371K9ra2rjxxhu9j02ePJn169czffp0mpubefTRR1m6dCn79u0jLy+v3/08/PDDPPTQQ2c9/s477xAVFTWIv8p/Nm7caHQTgpajRgGsbDlUzAbL6SHto/f5bXA1UNNRgwULpTtLqVbMtUaNbkbJS+QCp7qSOLRhg1+P1fv8pivplFDC0+8/zaywWX453ltHLICFyLYKNmwI3ITi2YxiNCc5uek5jhwL3IUjEJ8P3Y3a++yToxVs2FDql2O4VTd7mrRArOVICxtO+Pd1OVC+Or+ziv/JGOBEVxIFfn7P9ZbQngDAqztepftgt1+OccTz+kgOV9m2+b1BPXc45ze12ckSoOPEFt4N4DkNBFWFWLuVFgf89Z9vkxs79H2d7xy/3v46ADFNMWww2TlMbjnCRaqbdnsSG7fsAfyTFn/m+W1zalMltpdu99s5VVXYccoKKLSXHGJDg3/TIHsf+ApbHBHOZrb+8080xPR/b+1LwXAP3N4+sGrPIROI6c5c8FFV1QEtAvncc8/xk5/8hFdffZW0tDTv44sWLWLRop51IpYuXcqcOXN47LHH+O1vf9vvvh588EEeeOAB78/Nzc3k5OSwatUq4uICUPnpPBwOBxs3bmTlypXY7SZbT8dHptS28bdHP6ai08qqK1ZiG8Qk8f7O74bTG2Cr1jt4zRXX+KnVxrM9/ksAxi67gTFTVvvlGP2d34JdBTxz9Bms2VZWz/P9cVVV5aH9mwAHt6xaHNCUDWV3Dbz5EXmRDYxb7Z9z2lsgPx+WdTr44y8+oK5LYeHyy0mODvP5MU41naLzjU4irBHc/rnbsVmMvaT5+vza/vRzAMZd/AVyJ35m2PsbKPdpN9u3bqcltoXVq/zzujz5/kkoOMnSSZmsXj2wtEufnN/Oi1B/9b9Ed9ey+uJ5EJN24eeEkDea9rKxoJrwrKmsvmjsoJ8/kHP8+OuPQzdcu/BalmcvH2aLg4vlkxNwAiLGX8RqP3wmn+v8tjvaWfePdTSrzcxfMZ/UqFSfH/tUTRvtn35MuM3CHdd9hjBb4GYnWdtfgGMbWDrainuR/651wXQPrGfLXUjIBGIpKSlYrdazRr+qq6vPGiU70wsvvMAdd9zBiy++yOWXnz/NwmKxMH/+fI4fP37ObcLDwwkPDz/rcbvdbvh/vC6Y2hJsJqTHExtuo6XLSWFDF1NGDT547n1+D9RrpdVnp8827znvbvcuKmsbswj8/Hf2Pr+z0mfxzNFnOFh30C/nt6iujfo2B2FWCzPHJGG3+XmBy97GLgHAUrYbi0XRytoHQCA+H5LsdiakxnC8upVDFa1cNuX8n9NDccjTozstZRqR4QGa2zcAPjm/HQ1Qq63lZRu7xO/vud7mZmhFCgrqC1AtKmFW3wfR+8q0m5S5Y5MGfa6GdX7tyZA6GWoKsFftg0T/d4AE0tyxSWwsqGZ/WfOwXoPnOsfN3c2cbtYySWZnmPCaV7EbAMvoBVj8+LedeX7j7fHkJeRxtOEoBY0FZMZn+vyY+8pbAK1KaXTk2fewfjV6IRzbgLV8F9YAvGaC4R54oMcPmWIdYWFhzJ0796zhxo0bN7JkyZJzPu+5557j9ttv59lnn+Wzn/3sBY+jqip79+5l1KhRw26zCE5awQ4t+DpQOvz1xEZEoY6KvdqisrGjtMWcA2h6qtZbfrThqF9K++oT26dlxREeyCAMtBvC8DhwtEH14cAeOwD00cU9nqIMvmbqhZxLtXk4JI2H6MBWg8yJzSEpIgmH28HhOt+/Lt1u1bvGXMAKdfSWra/hZ76CHXqRHH+t4Xew5iCAOauU9qkMHJhCHb3p17p9tf6pWKoXTprtqa4ZUL2rBPux8FYoCplADOCBBx7gr3/9K0888QQFBQXcf//9FBcXc/fddwNayuBtt93m3f65557jtttu41e/+hWLFi2isrKSyspKmpp6br4feugh3n77bU6dOsXevXu544472Lt3r3efwpymZ/lmYeeW7haON2ijp6YOxLwXp3la5bEAyozOJCkiCafbSUFdgc/3r1eRMuSG0GLRzimYsqLULM96YntK/FPFbV+1iQOxssBWbutNURRmpPpvYedTtW00dTgIt1mYPGoYE5mGysSVE6dnaQs7VzR1UuWHhZ31IEF/fZhKUym0VoHFBqMC/5miL6Z+oOaAX/avdzrONeJalzlLO6+tVdp5Fl4hFYjddNNNrF27lp/+9KfMmjWLzZs3s2HDBsaMGQNARUVFnzXF/vSnP+F0OvnmN7/JqFGjvF/33nuvd5vGxka++tWvMmXKFFatWkVZWRmbN29mwYLArNkijKEv7HywfHiB2P6a/d41jFIiU3zRtODkXVTW2JvCA7W+v0DtK9FeA7ONuDgBZHkCsbJdxhzfj/QRsX0lTbjcvu0Fbe5u5mSTtpCzKW8Ke3d+GEAPbv0RiOk98zOy4wOzkPOZ9M+x8j3gdgX++H4UHW7zLuzsj5Fovby6qd9z6dPAHvhUZ/2cHqo7hNPt2+q9LZ0OjnuqlBpyrbNHQrpnYWe9k0kAITRHTPeNb3yDb3zjG/3+bv369X1+3rRp0wX395vf/Ibf/OY3PmiZCCX6iFhBRTNOl3tQBTt6GxFpiQana4DWU7ipZJPP11jpdLi8Cw7PzIn36b4HLNu8gdjE9Fiiwqy0djk5Ud3KpAzfjX6YOkXK7e61lpGxgZg/1jXaY2RaImgpwWEx0N0KNUd6bhBNYlZOAgUVzewtaeQz03y3Lqqqqt7OMFOOQgd4/bAz5cbnEmOPodXRysnGkz5do+1AaROqCtmJkaTGBnh+mC5rHlTs085z/rXGtCEIhdSImBC+MjY5mugwK50ONydr2oa8n73VewGYlTbLNw0LRnq6hmKFUbMMaYKeO+/rEbHDFc043SopMWFkJRhU7CHLM1+l9hh0NBrTBj+xWhRmZicAvl/Y2dQLOdefhM5Gbb2d9GmGNCE/OR+LYqGqvYrqdt8uybG7yIBFZXuzWCHT03lmwvTE2X5aTL24pZimribCLGFMSjThQs4GpgMDWBQL01K097uvR6L3ljYCMNPz2jCEiTsdh0MCMTEiWSwK+Zme9MQhzhNzup3sr9V6i00diOk3KhnTIMyYdfKmJU9DQaGstYy6jjqf7XevJ3VnZnbCgJbB8IvoFEgcq31fvtuYNviRfrPt6+IB5i7U4bkhzJwNVmMqf0XZo5iQMAHw7ZyV1i4nx6q06m2GpQNDr3li5kuTmuV5zx0o821KsD46OjV5KnaDXpd+4+yG8r3a9waNQgN+S8Pf5/n8neXpGDOEnoZfvhfMtAj4MEkgJkasacMs2HGi8QQdzg5i7DHeGxZTMjhdAyAmLIZx8eMA316g9gVDLyH0XKBKzddTqN9s+3K+iqqq3k4QU89VMfCGEGB6ijYSrZ9rX9hf2ohbhayESNLjIny230EzcSA2PjWGmHAb7d0ub9DrC3rnhynfc1UHwNUFkUmQNM6wZugFO3ydEqzPhTb0Wpc8AcLjwdlhyirBQyWBmBixpnlK2B8aYsEOPS1xesp0LIqJ30rem0JjC9joF39fXqC8vYRGB2LelA3z3RTq5/ZYdQstnb7pBS1uKaalu4UwSxgTEyf6ZJ9BRX/PZQVHIHaw9qDP9qkH5LOMSkvU6e+5miPQObCFV0OF1aIwI1vraPTlSLS5C3X0mpNpVHYEPWn4p5pO0dztm9dlZVMnlc2dWJSe+x5DWCyQNUf73oQdIENl4rtHIc5PL9hxqLx5SOkbeu+gqdMSnV3a5Fowvnc+1be9843t3RTWtQN4b1oM4x0R22m6NVZSY8PJToxEVWG/D9btg55R0SnJU7BbTJYi1WvxdCNHoaHnPXew9iAuH1UX3GPkchG9xaRBwmhANWVKsHc9MR+NRHc4OzjWcAwweTqwwe+5pIgksmOyAd91gOiZH1rxJINr9Mk8sbNIICZGrHGpMUTarbR3uzhdO/iCHaaeo6KrPBgU6RrQk7JxqPYQbtU97P3t8wQFuSnRJESFDXt/w5IxHSx2aK+FxiJj2+IHPemJvikeoN+g6CM2puJdPD0T4gO7ePqZxsePJ9IWSbuznVNNp4a9P1VVvSNihhXq6M3E64n5emHnw3WHcaku0iLTSI9K98k+g0qQpANDr3liPpqbGTSZH9C301EAEoiJEcxqUZiaqQ3TD7ZgR31nPSUtJUBPr7Ep6T3FWXMNTdcAGJ+g3RS2Olo53XR62PvrKdRh8GgYgD1CC8bAlBeonipujT7Znz4iplcYMxXvDeFcY9sBWC1W8pO10u6+mJtZ2tBBXVs3YVYL+ZkGpkjpTHxTqKd+HqtuobVr+GtS6WmJ01OnG1fYyF/aaqHBc03JnGNsW+iVhu+j7I+gmQsNfasEd/omQyLUSSAmRrRpQwzE9A/ICQkTiAsLghsKf9HTB7KMvzjZLDamJk8FfDNPLKguTmDqlA199GNPSSPqMFMvHS4HR+qOACYdETN4zb4z+XLpCH10ZsqoWMJt1mHvb9h6j4iZLCU4LTaCrARPSrAPRsVGxPywlEkQmWBoU6An++NAzYFhf1663Sr79UIdRlZM1MWk9qQEl5kvJXgoJBATI9pQKyfqgZip0xKh54MyCHoJwXc9haqqBle6Bpi6d35qZhxhVgv1bd2U1HcMa1/HGo/R7e4mLiyOnNgcH7UwiOiVM4MkEOt9Uzhc+nsuaDo/Rs0Aaxi010FDodGt8blZvTpAhkNV1Z6KiSkmDMQMXj/sTJOSJmG32GnoaqC0pXRY+zpV20ZLl5NIu5WJ6TE+auEwZZm3ONVQSCAmRjQ9EDtc3ox7EAU7RkQg1tWipQ9AUIyIge9uCvUUKbtVYcqoIBnR1EfEKvZpa9qYSLjNSr6nWteekuHNEztY0zM/zHQpUk1l0FJu6OLpZ9JHHY83Hqfd0T6sfXlHoYOhZx7AFj4iUoKHO0+sqr2Kmo4arIrVm5VgKkE0PwwgzBrGlOQpwPA7HfXOj+lZ8disQXLLn23e5VqGIkj+V4QwRl5aDOE2Cy1dTorqB3aT4VJdHK7T1sCYmWbiQKx8L6BCXLZWYSwI+OqmUL8hnDIqjgh7EKRIgVYMJTJRK45S5bty4cFido5v1hMbEfPD0vMNWzz9TOnR6aRFpuFW3d7PvaFwutzezIOgGRGDEVOwYzgpbnpaYl5iHlH24Hhd+ozb1WsUOjgCMfDdemI9KfhBMBda13tEzGQpwUMhgZgY0WxWC5NHDW6eWKWrkk5XJ3FhcYyNG+vH1hksiOaH6dKj00mL0m4KD9UdGvJ+egp1JPimYb6gKD0TmU04T8ybJjXMyommrpgYZPPDdL6YJ3asqpVOh5vYcBvjUqJ91bTh08+1CdOkpmXFY7Mo1LR0Ud7UOeT9mPo9V3sMulvAHg2pU4xujZe3cuIw52YGXTowaCnBFhu01UBjsdGtMZwEYmLE8xbsGODCzsUu7YNjRuoMcy/k7K2YGDyBGPSkgw7nAhV0hTp0Jp4npqdJHSpvptMxtDWp2hxt3jLq+Sn5vmpa8NDnZAZRzzz03IAP5z233/Oem5ETj8USRCmleudHxX5wDD1YCUYRdiuTR8UCw1tPTP9/N2Ugpn/WZs0Bq8FrbPWin+uC+gK6XF1D2kenw8XhCm1R6KDqdLRHQrono8GEHSCDZeK7SCEGRl/YeaAjYsVOLRCblTrLX00KDmV7tH+DpFCHzntTOMR5Yr1TpIKmUIcu27yTmLMTI0mJCcfpVjk0wE6PMx2uO4yKSmZ0JimRKT5uocFcTm0NMegJDoKEL3rng25+mC5xLESlgNsBlb4pFx5MetIThzYS7XL3pOKbOh04yDo/smKySIpIwul2cqT+yJD2UVDRjMOlkhwdRnZipI9bOEwyT8xLAjEx4k3zBmLNA8qjL3Fq64eZen5YWy00eVIGMmcZ2pQz6YHYUCcxB22KFPTcgNedgA7fLH4cLBRF6SljP8TeeVPPD6s5Ao52CIuF5DyjW9NHfnI+FsVCZVslNe01Q9rHXk8J7RnBFogpSq95YubrAJk1zLmZp5tO0+5sJ9IWybj4cT5sWZDwjogFVyCmKMqw54n1TksMusJGUjnRSwIxMeJNTI/FblVo6nBQ2nD+0to17TU0qo1YFIs50zR0eopUykSICKJJvsDU5KlYFSvV7dVUtlUO+vl6BbGgS5ECiErSinaAKeeJzR5mOW1Tz1XxzsmcDZbgujRH2aO8N+FD6QBp73ZyrKoFCMJRaOhZPNvEBTsOlDXhcLkH/Xy982Nq8lSsliApbOQrXS1Q7SlAE2QjYtBrJHqI2R/7SoNo/bAz9a4S7HIY2xaDBdenvRAGCLNZmJSh5dFfKD3Ru5Bz/ASi7UE2muJL+k1hkKUlgnZTOCFhAjC0VClvL2EwXpyg1zwxEwZint75oc5XMfWImHdOZnClJer0m0I9GB6MQ+XNuNwq6XHhZMRH+Lppw2fiEbFxKdHERtjocro5Wtky6OebuvOjfA+gQvxoiM0wujVn0YvkDDX7o2dELLg6UwFIGq918jo7TVkleDAkEBOCnnliF1rYWf9ANOWilr0FaaEO3XB6CoO2UIfOWznRfDeFM7LjsShQ1thBVfPgCiPUtNdQ2VaJRbGYcy2jIO78gOHNzQz6zo/MOYCipWO3VBndGp+yWBTvqNhQRqIP1mk3yabs/PDODwvOzo9pydNQUChrLaO2o3ZQz21qd3Cqtg0I0vedxdJzrTNhB8hgDCoQczqddHd3D/jL6XT6q91C+FR+pmeeWHnzebcbEYGYqvakJgb5TeFgewrbunpSpGYHayCW3atyosnWWIkOtzExXRt9HuycFb1nfnzCePOtZdTdDlWeFKkgHRHT33MH6w7icg+u6uXeYCyh3VtEHKRO1r43YQeId2HnQb7nulxdHKs/Bph0REwPAIJsuQhdTFgM4xPGA4PvANlf1gjAmOQoEqPDfN003/DOEzNf9sdgDKpW54IFC0hISLhgQQPl/7P33+FxneedN/45U4BBHfQOEAAbQIIk2CSS6o0qlnuRS2Q7sbXx5nUSx/H7S7ybXHGcTRy/aV5n1/Fu5BbLcbdky5IoUb2wF5AECIAkCBC990GZdn5/nHlmhiTKAHNmTsF8rksXRuDMOTdm5pznudv3liRkWcblcnHixImoDEyQIB6EKyfKsrxgY6vb5+biqLJZEhkZUzLRBTPDypyPIn0uvuL9vzhyEa/fi80S2a2ssWcCvwzFTgcFmToskQLlPbcmwewojLWHesZMws6KbFr6pzjbNcZDdZGXA5laQrv/PMg+SC+CzBKtrVmQ9VnrSbGl4PK4aJ9oZ0P2hohfe75bpyql4ZTtgaFmJUtS8y6trVEVMcNvpcqJLaMteGUvOY4citOKY2CZhsiybuf2hbM9fztXxq9wYfgC91TcE/HrdJ+FhuuDjmuYFTlisizz6quvRvz8vXv1++VOkCCczUUZ2CwSoy43fRNzlGTdLPXaPNqMx+8hVUqlPL1cAyvjhMiGFW4Fuz6dlSpnFen2dKY901wZv0JNTk1Er2swwuJkS1acsZ7TymdhNkesPIsfn+gMbhQiRWTEtuaaeH5Y6W5FxU+H2Cw2tuRu4fTAaS4MX4jYERt1uekcnQFCCrW6pGwvnP2hKTeF4n7XNuRiYtaDM8Ue0evENVeXV6c/1b1oGe9UBgpb7FCk38Dqtrxt/Oryr1asnChUSnWbhYYwleDLMDsOKVlaWqMZKypNXOmFaLoLN4FpcditbAyUTC3WJ3Zu8BwAFdYKc3+3dd6rAmCRLMGehZUsULrvDxOYeLCzeO8vdE/g80dWeumX/cFeFVNmxMIVE3WMKMleiUiOuOaq89MidgA0ITjD7wyssPRS7+SmJ1ORo5TzriQAEu6ImQ5RglpUp9uAI4SJ5KygJFiW5WDQsV6PQh2CtDxljh+E+tLXIAmxjgQJAtSVZALQtIgj1jDUAEC5zcTZMAgoSaFboQ5BUDxgJZvCYJRQx4sTmHqw84aCdFKTrLjcPtqGpiN6TedkJ1PuKZKtySsqiTMMQUdMn/1hAqHitrJrbhyAej1noUHpEbOngccFQ61aW6M6ocHO4xG/xtSKiT36VikVrHeuJ9WWisvj4urE1Yhe0zcxx/D0PFaLFOx/1y0mVgmOlIQjliBBgLpllBPPDQUyYraKuNkUd/x+6G1QHus4IwahSGGkGbHBqTl6xmeRJB0Olb0RsTnoOw9et7a2qIzVIgV7MiPdFIqNf21OLXaLjrMqq2Em0AsIUKLvjJjYkF8eu8ysd+mZi4JzehfqEFisofffhOIBK3XEJt2TdEx2AGugHFjHWC3WYEYy0gCIuOZqijJw2HU++83EQcdISThiCRIEEI7YQsqJ/a5+BmcGsUpWSq2l8TYtfoxcBvcU2FJCKmI6RSxO7RPtuDyuZZ9/PpAN21iQTnryitpj409ONaTkgG8eBlY3zFPPiE1hpGVSpi6REiU5OeshJVtbW5ahMLWQ/JR8fLKPiyMXl32+LMuhobJ6d8QgVAVgRkcsKNgxvqzgGhD8fMvSy8h26Pt7uWJ8XuhrUB7rPOAIrNgRazBKCT5cX4ZvMpXgSFnRbiQ3N5cDBw5E/Py8vLwVG5QggVZsKc7EIsHQ1DyDk3PXqeo1DDYAsCl7E0l+nUrBqkGwP6werPp2VvJS8ihOK6bP1cfFkYvsLVpaHMgQQh0CSVIitVcOKyUbOo/arhSxQRD9Q8uRKJHSB5IksS1vG692vUrjcCO7C5e2uXtsllGXG7tVorY4I05WRkFwhp/5HLEtxZnYrYogVdfoLBW5S4+AaBptAkx6zQ23gmcGkjIgb6PW1ixLcHREhMPUDVMODIowlcWuKDWPXwv1jK0hVpQR27NnD+vWrYv4v9279b+wJEggSEmysqEgHbi5PFGUJZp6fhjofn7YjawkUmgYoQ6BiUs2xGfQ0jfFnGfpBnSPz0PzaDNg0k2hQfrDBKJPLJKSYHHNbSnOJNmm8xIpCH0GA03giaz00ig47Fa2BPqFzkYgY980ojhipsxCB9e5eqUkVeeIzyCSkmCfX+aCkbLQdocimAKmFKeKhBWFvF988UWeeeaZiNLaAB/+8If5m7/5m1UZliCBFtSVOLk0ME1jzyT31RYGfx/uiMmDJk6fizIpnQt1COry6jh87fCykUK/Xw5FCY2wOIGplRNLnA7y0pMZnp6nqXeS3esWL326NHYJj9+DM9lJWUZZHK2MA7JsPEdsBSI54prTfU+mwFkGaQXgGlT6Mytu1doiVdlZnsW5rnEausZ5b/3iJfayLJu7HDh4zRljnRMlwUOzQ7SMtrCzYPFe0rahaVxuH6lhgWXdU7pHEQnrOQ3bPqS1NXFnRRkxWZapqKiIOCMWqcOWIIFeWEiwY847R/OIEpHfkbdDE7vigtcN/YHNlc5FAwR1ucomYTlHrGPExeScl2Sbhc1FBiiRgtAmYbRNEXQwEZIkBWWVzy9Tnig2/KacZTTRHZhlpN/h6TeyNXcrEhJ9rj6GZ4eXfO45I8wyCkeUBIMpyxOFWuxyvZmT8iTDc8NYJWvEMxoNhcGCH5Ikhao/hpYOgIgS/G2lTqwWg9wv1/hg58QcsQQJwhCOWFNvyBG7OHIRr+wN9iSZlsEm8LnBkWWYIcJbcrdEtCkUJVJ1pU7sVoNoFKXmKAIOECqlMRGiV2+5TaFwxExdlqjj4ek3kp6Uzvos5Xu51KbQ6/MHA1q6nmV0I2Z2xALXXGPvJB6ff9Hn9Xh7ANiQtYFU+9K9ZIbDMwuDAaEZg5TgQ9g8sWWCjsHKj4A4iyEQ1R9950ynEhwJBtmRJEgQH7aUZCJJoTkcECpL3JG/w9zBhfByDYP8nelJ6VQ5qwBoGm5a9HkNneOAQYQ6wlkDfWJCVW8xzC3UYazIvCCS8sTLg9PMenykJ9uozjNIiRSYWjmxMjeNTIcNt9dPa//Uos/r9nUDJi1L7L8Afq9Sguo0TqlzpP3QIuhoCKEOQe56cDgVleDBxddxs5JwxBIkCCM92UZVXhoAjYForlBMrM+v18iqONETGORsoCghRLZANQSblw0UmYdQpNCEGbHtZcpn0T7sYnxm4SjolHuK9gllxpYpZxn1GvuaOz+8uGBHqD/MicUoJVIQKsseazddSbDFIkWkWGpqR6wnrA/aIAFHCN3/uqe7GZtbWGxlzuOjpU9xsA1TDgw3lASbb61bjhWJdciyzFe/+tWIn5sggRHZVurk6pCLxp4J7tqUH8qIFZi4PwwMJ9QhqMur4zdtv6FxZOGSjXmvj+bAbLid5QabhxMenZdlQ20cliMrNYmqvDTah12c757gzk35Nz3n4shFZGRK00vJTcnVwMoY4veFHDGDZcREmVTTcBN+2Y9FujmmaziVUoEoCR5tUzaFG+/X2iJV2V7m5K3Lw5zrGucTt6676d/9sj9YmpjIQuuHjKQMqpxVtE+00zjcyB1ld9z0nKbeSbx+mfyMZIqdxih1DlK6G9peVa65vZ/R2pq4siJH7Fvf+haTkzcPu12MBx98cMUGJUigNXUlTn7d0EtjzyQ90z2MzI1gs9jYkrsFFi+rNzZuFwy1KI8NFp0Pn7Eiy/JN5aMtfVO4fX6yU+2U56RoYeLqKawLm7HSCdk3b5yMzI4yJ+3DLs51jS/oiIULdZiO4UvgngZ7GuRv1tqaFbE+az0Oq4NpzzQdkx1UO2/uKQ0KdZQZLAsNyqZwtE3ZtJvMEQv1Zi5cEnxt8hrzzOOwOoK9gKai11gjWsLZlrdtSUfsXNisTMO1UZSYtyR4OVbkiO3fvz9WdiRIoBu2lmYCinJiw1AfAFtytpBsTcbj92hpWuzoOweyHzJKINNYgiSbsjdht9iZmJ+ge7qb8ozy6/49OMi53ICLk5ixIqR9zeaIlWfxTEPvomVSa6I/rGSnIWYZhWO32KnNreXs4FkahxtvcsRm3T5aBwxYIiUo3Q0XfmbKTaEY33F5cArXvJe05Ou3gWJ+WE1ODTbLiraI+md2DEauKI8NVvkBoeqPxcrwG4IjWowY/Ah8HkMtMD8FyQZRN1aBRI9YggQ3sDUw9LJnfJYTgdIhUYpjWgw2VyWcJGsSm7OVjMJCilLhUUJDYmYVt8CmsKFrYsFydlNnxIK9KsYYFXEj4jNZ6Jpr6p3A55cpyEimKNNgJVJw/TVnsjaLgkwHxU4HfjnUBx2OKPHemmPinszsKqUE1WDcWP1xI4YtBwbIKILMMkBWAsNriIQjliDBDThT7KzLVSR7T/UrN27T94eJTaFB5ofdyFKbwgahImXExQlCm0KxiTARW4ozsVkkhqfn6Z2Yu+7fBlwDDM4MYpEs1ObUamRhDDFor4ogfFN4I4bOQoMy081iC5UEmwwhlLNQJlpkxMSMRlPRY8w+aIGo/hibH6Nnuue6fxtzubk2MgPA9tIsDaxTARMrli5FwhFLkGAB6kqdILnpdrUBa0Ax0aBCHYLFHLGJWQ9Xh1yAQaOEcL0j5vNqa4vKOOxWaoqVEpQb54mJyLw5ZxnNwUDgu2pQR0xccy2jLbh916teipEEhg1+2B1KfyaYclMYVE68oU/M7XNzafwSAFvzTJgRCzpixrzmkqxJwQHbN651wqmuzkvDmWqPt2nqYOLqj6VIOGIJEixAXYkTa0oXMn4KUwspSivS2qTYMTMKYx3KY4NmxER0vnm0Ga8/5KxcCGwIK3JSyUlL0sS2qMndCEkZ4JmB4VatrVGdxQY7m7o/LDjLKB+c5cs/X4eUpZeRlZyFx+/h0til6/7N8OXAYOpNoZgx1XDDNXdp7BIev4dUKZXStNL4GxZrDCzUIVhsXEtQHMeowQ8Iy4itLQn7hCOWIMECbCt1Yk1RSlJ25Ju8LFEsTjnrIcVg8u4BKp2VpNnTmPXO0jbeFvx9Q5cyb8XQi5PFAiX1ymMTbgpDfWLj1/3e1P1h4RtCI5buAZIkBbMm4ZvCUZebzlGlRGqbERUTBSaea1RX5kSSlD7o4en54O/F51hqLTVmSelSTPbCVB9IVig2bs/3YsPUg/1hRr7miusBCSa6YHpQa2viRsIRS5BgAbaWZGJNuQZATbYJI/LhGLxuHsAiWYIDL8NLNhqMLKEdjpmj8wFH7EKPIvAAyiyjpmGlV8WUGTGD94cJFuoTOx9eIpVi0BIpCH02fQ2mKwnOdNipzksDQp8XhD7HMmuZFmbFFnHNFdRCUpq2tkSBCEw1jzQHVZxlWQ5loY0cdHRkhkZ5mDAAshgJRyxBggXISrVjT+sCIFW+eUaOqegxfrkGhHoaRG+RLMthcr5ZGlmlEiZ2xNbnp5OWZGXG7ePK4DQAHZMdTHumzTvLyMSOmClKpADywkqCxYxFExGuWCoIOmI2Mzpixg84AqzLXEeGPYM531yw+qN7bJYRlxu7VaK2OFNjC6PExGvdYiQcsQQJFqBzqhPZ4kL225iaKNTanNghy4YX6hDcuCnsm5hjeHoeq0VSxFeMjFicBi6CZ1ZbW1TGapGCJWwiqis+wy25W0w4y2g8NMvIoD2ZApGFbp9oZ8qtzA0zRYkUKLPdTFwSXB8U7BgHYMo9RftEO6CUJpoOkwQ/LJLlppJgcc3VFmfisBtrJuFNrEHlxIQjliDBAjQMNgDgnyvlYu+MtsbEkslemB5Q6uaLjFs3DyG55ctjl5nzzgU3GDVFGcZfnDJLIL0IZB/0ndfaGtUJRucDG4oLQ2buDxOzjCohLVdTU6IlNyWX0vRSZGQujlw0T4mUwMTReSGkcr57HFkOfH7IFKcVk25J19Y4tfH7obdBeWzwyg+4OehoCnEcQUmYI2ayGX6LkXDEEiRYgHNDykBB3+w6GntvHnppGoJ181sgydgS4UVpReQ6cvHJPlpGW66bZWR4JMnUkcL6G5QTTa2YaJLIvCBcxc1UJVJgasGOmuIM7FaJsRkPXaOzIXEcM84PG22D+QmwpSg9YgbnRuVE05QDgzI2wpoEc+MwelVra+JCwhFLkGABQo5YBe3DLqbnzdWsHSRYlmjsEilQVNzCI4XB/jAzRAnB1I6Y2EC09E8xOTdLy5jSk2PKjJjBZxndSPg1Z6oSKQh9RoMXwe3S1haVSbZZ2RJwlhu6x4PiOKLc1FSIa654O1gNLCATQFxzbeNtTM5Nc6FHzO0zeDkwgC0pVJ0jqgdMTsIRS5DgBqbd01weuwxArm0jsgwXeyc1tipGmESoQyBq588PXQguTqaIEoKpy6SKnQ7yM5Lx+WVevHwGr99LdnI2pekm7FUxwSyjcMTG/cLwhWBGc7vR+8MEa6Qk+FzXeDC7Yk5HzFxZ6PzUfApTC/HLfg63nWHW4yM92UZ1nklKSk281i1EwhFLkOAGLgxfQEamNL2UbUUVyu96TFieGF43b3ChDoGIFDYMXmDG7SMtycqGApMsTkLYYaxdGcJtIiRJCvY3vNmpOCp1eXWJWUYGYEvuFiyShcGZQU51KyM/TNGrAoGSYPNuCsXndLq7g4GZASyShdps45fu3YT47EwS/IDQWvdWp/K3bS9zYrGY5H5p4mtuIRKOWIIENyDKErfnb2dbQG2vyYyO2OjVQN28Q+kRMwEimts30wWWGbaVObGaZXFKyYbcDcrjXvP1rIiymubRNTA/rGCLoWcZhZNqTw2OGGgdUz47w4+LCMfUJcHKNdc6fhGAamc1qXZj9wrfhNcN/YHhxyYJOEKobPti4H5pmsoPCH1OfefA59HWljhgOEfsW9/6FlVVVTgcDnbv3s1bb7215PPfeOMNdu/ejcPhoLq6mm9/+9s3PeeXv/wlW7ZsITk5mS1btvD000/HyvwEBkAoJtbn11NXqtTQm1KwQ2wsisxRNw+Q5ciiPKMcAGtKj7kWJwhTlDKfIyY+qyG3Iu1uzv4wUSJl/J7McITT7LV3KiVS+SbJQoOpo/PVeemkJ9vw2TsBkwY/BpvANw+OLMgxz0xQ8VkNupU2ClMFP3LWQ7ITvHNKf6bJMZQj9tOf/pQvfOEL/Pf//t85e/Ysd9xxBw8//DCdnZ0LPr+9vZ1HHnmEO+64g7Nnz/Lf/tt/44/+6I/45S9/GXzO0aNHeeyxx3j88cc5d+4cjz/+OB/5yEc4fvx4vP6sBDrCL/uDGbGdBTuDGbErg9PMuE0m2GGS+WE3IlS/rI4u8wh1CEy8KdxemgWWOfy2AcCsjpi5hDoE4rOyOrrYVmqiLDSESoLHr4FrWFtbVMZikdhe5sTq6AbMfs3tUkpNTcKW3C1ISPgso0jWKXM5YhZLKFhlwqDjjRjKEfvnf/5nPvOZz/DZz36W2tpavvGNb1BeXs6//du/Lfj8b3/721RUVPCNb3yD2tpaPvvZz/J7v/d7/OM//mPwOd/4xjd44IEH+PKXv0xNTQ1f/vKXue+++/jGN74Rp79KXaa7jjIw14pf9mttiiG5Mn6Fac80KbYUNmZvpCBTERHwy9DcN6W1eepiMqEOweZspczSktJlvoxYuCNmshkrzlQ7ZYXKRjcvuZhsR7bGFqmM3x9SATOZIyai89aUbraXmUC2PpyULMjdqDw24aZwe1km1pQuwOyOmLmuufSkdEpSKwHIzR2kMNOhrUFqY+Kg443YtDYgUtxuN6dPn+bP//zPr/v9wYMHOXLkyIKvOXr0KAcPHrzudw8++CDf+c538Hg82O12jh49yp/8yZ/c9JylHLH5+Xnm5+eD/z85qSjqeTwePB7t6ll9fh/3vfn/4JHg4adeoWTLB/BvfhTSCzSzyWic7lMu+m2525B9Mh6fh63FGbw+Nc+5zlFq85MBNP2cVcHnwdZ/HgnwFG4Dnfw94n2N5v1N8iqlifbUbnJTLMb/rMLJq8FmsSG5hvCMtIOzfEUvV+P9jSW5uf1M+CBDqtatjUux5Ps7fBn7/CSyLQVv9gbdXHNqUJFWAbIdyTpHYc5kzD47rb6/1pKdWEYu4+s6gb/qnrieO9YU5kwiDc6BbKMyvVL394iVYus5jQR4C7cj6+BvUvP9zZSq6KGdvNx+03xeAqlwBzZA7jmNdwV/m56+v5HaYBhHbHh4GJ/PR2Fh4XW/LywspL+/f8HX9Pf3L/h8r9fL8PAwxcXFiz5nsWMCfO1rX+Ov//qvb/r9Sy+9RGqqdo2uNt8MJT64ZoOuoQbKDx3DcujPaMs/SFPpx0AyVAJUE553PQ9A2ngazz+vPE52WQALL564SO6oMmj28OHDWpmoCpkz17jHO4fHmsrzx1pBuqy1SdcRzft7oteDnGJBsk7x0+d/itNiEintAHcll5E120HDb79Db/YtqzqGXr+/0+4msMLYoDN4/RmRhd7f8pG32QWMJpfx9qGX4m9UDHH7wDdbgjX1Gi1XD/H8wEBMzxfv72/VWDLbgeFzL3Js2lx9VC2uBkD5/H773GGSA+Pf9HqPWAk23yyPDCkzCV9uHmP+in7uKWq8v8N9GZAJM94WQ98vF8LhGeNBgMFmXnz2aXzW5BW9Xg/f35mZmYieZxhHTHCjnLEsy0tKHC/0/Bt/v9JjfvnLX+aLX/xi8P8nJycpLy/n4MGDZGZqW5Zx6I0jXOt5ncu1j7Kv/yqW3jNsGHqR6qIsfI/+T7AY7iOPK9/+zbfBAx/c/0EOlBwAIKl5kBf/s4EJSyYPPLCXw4cP88ADD2C3G1fgQjrzA2gFa/keHnnXo1qbE8Tj8UT9/r700/P4XYVYHX0U7ijk3vJ7VbZSWyzSa3Dme+wqkqm/75EVvVaN9zeW/OMv/gXcMDVTzcMPP2w4+fql3l/Li29CJ2RtvY9HHljZ56Z3znSO4/v1C1hTr5FZZeORPbH5+7T6/ko9RfD9H1Lg7eGRhx82Va9R46lGuAS+uXLKt+9nR0m6ru8RK0G69g7SeRk5o4T73vtxrc0B1P0O//03xwGYT+435P1yOeRrf4801cdDO4qQK/ZH9Bo9rXGiWm45DLMrz8vLw2q13pSpGhwcvCmjJSgqKlrw+Tabjdzc3CWfs9gxAZKTk0lOvtk7t9vtmn/wVc4qXu95nWt567C85ztw/mfw9OewXPgpFu8MfPA7YFtZZGGtMDw7TPd0NxISu4p3BT/LHRU5AFwecuEPtFXq4bOOigFFkMRSthuLDv+OaN7f870T+JLKsDr6aB5r5sHqB1W2TmPK98CZ72HtO4d1le+RHr+/A64Bxt0jyLKF8fECBl1eyrKNKaW94Pvbp/SHWcv3rvpz0yuNfdP45pQy2ebR5ph/t+L+/S2rB4sdaWYEu6sXsivjd+4Yc3FMUaXzzZbR1DfNnnVKb6Ye7xErZkAZwi2V7dbd3xLt+zsyPU/fUDbpOTZmmKJ/rp+KzAoVLdQBpbuh5bfYBs7B+jtX9FI9fH8jPb9hatWSkpLYvXv3TenGw4cPc+DAgQVfs3///pue/9JLL7Fnz57gG7TYcxY7pt6pzKwEoGOyQ/nF9o/AYz8EaxI0Pws//hh45xd9/VpGyNZvyN5ARlJG8PfFTgc5aUn4/DItA9MaWacyPQHRAJMJdYxMz9M1Oos/sClsHGnU2KIYIJqYe8+C36etLSrSOKx8Vsn+EpCTONdlopER3vmwWUbmEg0AONc1jm825Ih5zDb7x5YMRYGSRBOJB3h8HlpGlNI932w557pNdM1B2LgIE15z3eOAlSSfct1dGL6gqT0xYY0IdhjGEQP44he/yJNPPsl3v/tdmpub+ZM/+RM6Ozv53Oc+Byglg5/85CeDz//c5z7HtWvX+OIXv0hzczPf/e53+c53vsOXvvSl4HP++I//mJdeeomvf/3rtLS08PWvf52XX36ZL3zhC/H+81ThJkcMoOZd8PGfgT0V2l6BN76uiW16RzhiO/Ovn/EjSRJ1YrBzb2SpZl3jngnN5jCZdP35wEai2KGonDUNN5lPQTRvEySlg8cFQ61aW6MaYiNRkrIZEBsNkzDQCD43pOSYKpsiONc9juzJIc2Wicfv4dLYJa1NUp/gptA8yomXxy/j9rtJsaYje3I51zWutUnq0mtOZWCAhi6x1m0CQoEsU5FwxPTHY489xje+8Q2++tWvUl9fz5tvvsnzzz/PunXrAOjr67tuplhVVRXPP/88r7/+OvX19fzN3/wN3/zmN/ngBz8YfM6BAwf4yU9+wve+9z22b9/O97//fX76059y6623xv3vUwPhiA3NDjHtDsverL8H3h8YZv32v5j+i70azg4pWaL6gvqb/q2uROn9M4Uj1n8eZB+kF0JmqdbWqMrZwEZiV3ENDquDac801yavaWuU2lisodlGJrqOxUZCSKE3mGlTaNJZRgBjLjfXRmYAKSh/nojOG4PQNVcHSHSOzjDqcmtrlFpMD8F4JyBBSb3W1qiOcJq355v4mhOf23in6Wb4hWMoRwzgD/7gD+jo6GB+fp7Tp09z552hutHvf//7vP7669c9/6677uLMmTPMz8/T3t4ezJ6F86EPfYiWlhbcbjfNzc184AMfiPWfETMykjJIl9KBG7JiAFveC3UfAtkPT/9X8MzF30CdMued4+KIkiVayBETg52b+kzgiIXPDzPZplAsTrsq8qjJqQHMGikMRHhNsin0y/5gGel91cqG90L3BF6fSbKZJp1lBKHMZVVeGvUFihNtyk1hsCS4AXxeTU1RC/E57SjYRnVeGgCNZgg2QigblrcRHOZSzpVlOXjd3VO1B4DmEROWBDucSgUImCoTfSOGc8QSLE+eJQ+A9on2m//xkX+AtAIYboXX/y7OlumXiyMX8fq95KXkUZZedtO/i9LESwPTeI2+N+wNi86biPDFaUdZVjA6b05HzFzR+Y6JDlweFym2FO5Yt5X0ZBuzHh9XhszSkxn4nExYIiV6+XaUOYPZzKbhJi1Nig25GyA5E7yzMNSstTWqEJ6F3l6mrHGm6RMzcfCjc3SG8RkPSVYLd1XVkpmUidvv5tK4mUuCzbHWLUTCETMh+dZ8YBFHLDUH3v0/lcdH/hW6TsTRMv1ydjBQlphfv6AEbFl2Cs4UOx6fTF9koyH0S4856+bDF6ea4gxzO2Lisxu8CJ5ZbW1RARGZr82pJclmD20KzVCeODcJw4ENksmCHwDnRfCjPIuteVsBuDpx9frSeDNgsZiqJNjlcdE23gZAXV4dO8qzgFCfreExcfBDlG1vKckk2W4NBkAah0y41iUcsQRGRGTEbipNFNQ8Ajs+ppQoPvsF8Bs9xRM9DUMNwMJliSAEO5Q+sW6Xgcv5ZsdgVFl8zbYpvG5xsoUWJ1OquDnLlMy23xtS4zMwwhETn9n2siwg1JBuaPoaABmcFZBeoLU1qnJdFro8i7yUPErSSpCRg6XepsJEm8KLIxeRkSlKKyI/NT/kiPVMEBi3alxkOazyw3wZMZGFrg98ZubuzQwrwzf8F3NhEo6YCVkyIyZ48O+UMovBJmj+dZws0yeyLHNuUJmrtZgjBqHyxC4jO2K9Adn67EolO2oihCMmFqfyjHIykwIqbmYr2ZAkU20KRdayLtB4Xl9uooxYUELbXIEPgJ7xWYan3dgsEluKlUCVuTeF5lFODF5zucrntaU4E5tFYtTlYczoeh3j12BmBCx2KKrT2hrVCQU/lPtkMCNmxuqPwjrlc5wdhbEOra2JCQlHzISIjNi1yWv4FpszlJoD+/8f5fHrf2+qeUQr5drkNcbmx0iyJLElZ8uiz6srUW56hs6ImbQsEUKbdrE4SVJIxS1RsqFf5n3ztI4pMvxiQyGi860DU8y6DX5vMrEjJiLztcWZOOxWwOSbQnHNDV4Et0tbW6JEOMriHumwW6kpVuZnXps28BoHoXWuqE6ZAWciPD4/jT2iLzMLwNwlweEz/HqNHwBZiIQjZkKyLFkkWZLw+D30Tvcu/sR9/1VRpRlqgcZfxc9AnSH6w+ry6rBbF5+ELpQTe1zKzdCQiIyYyTaFHp8/qPYlFicIbTLMOdjZHMqJraOteP1echw5lKSVAFCU6aAgIxmfX6ap1+DliWJ4uhlLpAKRedHTBybPiGUWQ0aJUtbfd05ra6JCCKoIxxlC987OKaM7YuYd5NzaP8W810+mw0ZlrqJ0uXZKghOOWAKDYJEsVGRWANA+uUR5osMJB/5QefzG35tGknelLNcfJqjISSU92YZXlmgbMmg01KQZsdb+Kdw3LE4QKrsxZXReCAeMXoWZUW1tiYLwyLwQypEkKZgVM/Q8sal+mOwGyQLF9VpbozoNwSx0VvB3W3K3YJEsDMwMMDQzpI1hscQEAZDh2WF6Xb1ISGzJDVWBiM/RNBkxk61zwHU9mRZL6HMydQDEJNUfi5FwxExKVWYVsEyfGMCtn4OUHBi5Ahd+HgfL9EfDYAMAOwt2Lvk8i0ViS6B0w5CzVib7YKo3sCncobU1qhK+IVxocWobb8PlMajzvBipOZBTrTwWmU4DEuxVybu+l0P0+hlaTltsCPM2Q3K6traojM8vB0uk6sMcsVR7KtVO5XtpygCICTaFIhtW5awiPSn0vRSfY5dL+XwNic8bEMjBlBmxYAl+WOUHrJGSYBPN8Asn4YiZlHUZ64AIHLHkDLjtj5XHb/w9mE1dbhkm5ie4OnEVgB35yzsndSVKQ/pFIzpior46v8Z0m8IbhToE+an5FKYWJko2dEz4LKNwxEbD0IIdJi6RujI4zYzbR1qSlfX5199PxGeZiM7rkxv7wwTr89NJTbLi9ku0GXWG33AreGYgKV0Z5mwyblRMFJg6I3bdDL8Wra1RnYQjZlIqMyuBCBwxgFuegNQ8RZFmjfWKnRtS6vwrMyvJdmQv+/wtAUfMkBkxM5drLBIlBMw9ZNbgm8KJ+YngmA1RRirYFug76hydYdRlUBk3kw5Ph9A1t63MidVyfSmbuWf41QMSjHfCtDFLL0XP7I3BD6tFCgYbz3UbcI2DsHVuJ1is2tqiMtPzXi4NTgGwvdx53b+FlwQPzgxqYV7ssFgC1x2GXeuWIuGImZRKZyWwxCyxcJLSYN/nlMcn/m/MbNIjwUHOy/SHCcQi1dw/ZbzSjeCmcOkSTKMxNefhSiB6e+PiBCFFKVNGCsMdMQPOWGkaUZzj8oxyshxZ1/2bM8VOdb7S7yf6IgyFLJs6I9YQ1qtyI8EyqZFG/LJBhY0Ww+GEvE3KYwOquMmyvGgWGkLCK+d7DFoSHBzkbK51DqAxMOOtNCuFggzHdf+Wak9lfdZ65XlmDIAYPOi4FAlHzKRUZlQCMDo3ysR8BDfUXZ8GaxL0nDJ0mdNKibQ/TFCZm0qSRWbO4zdW6YYsh/qITJYRu7DE4gQmr50v2gYWG7gGYaJba2tWzGL9YYJ6I5cnjl6FuQmwJkPhVq2tUZ2lstAbsjeQbE1myj1F52RnfA2LBwbeFHZPdTMxP4HdYmdT9qab/n17qRJsvGB0R8zEWegdCwQcweRrnQnK8Bcj4YiZlFR7KgWpBUCE5Ynp+bD1/crjk0/G0DL94PF7gjesSDNiVotEWUCUr9FIC9VYO8yOKc52obkGXC5WMy8QqmC9rl5GZkfiZVZ8sKdAQUD1zICbQpGlXCgyD6FsiyEdMfF5FG+HJcZiGJE5j4+WfqVEaqGMmN1ipzanFjBrJtq4yoni86jJqVlwXIvIiLX2TzPnMdgMP88sDARK0Ev3aGtLDAgqJi4Q/ACT94ldN8NvRltbVCbhiJmYKqeinBhReSLA3ieUnxd+AS6TbVgXoGWkhTnfHM5kZ7CnLhLK0pQSsMYeA9XQBwdcbgNbkra2qExD1xiweJQwIykjeC2IUjhTYdDovCzLXBiK0BHrnkA2WumluOZMWJbY1DuBzy+Tl55EifPmLDSENoWmv+YM9r0U/WGLZaFLnA7S7TJev0yT0Xqh+86B7IP0QnCWaW2N6oig40LBD4DtedsBpR/adCXBmSWQXqR8vv3ntbZGVRKOmImJWMJeULZHmXXjm4ez/xE7w3RCcH5Yfj0WKfJLoTxdOGIGyoiZWqgjsDgtEiWENVKyYTAJ+4GZAUbmRrBKVmpyahZ8Tm1xBnarxKjLTffYbJwtjBIT94ed7RwHlCy0mP12I6aOzhfWKdUFs2NKtYGBWKo/DJQZfusCa5zhMtHh19wi30ujMjg1R8/4LBYJtpUuHHRcn7Ueh9XBlGeKa5PX4mxhHDBo0HE5Eo6YiRGCHUKefVkkCW75L8rjk98Fv8HKElbISoU6BCIj1tQ7gd8ogh0mVW/rn5ijf3IOiwR1iyxOAFtz14BgR+9ZQ12z4rPYlL0Jh23hrEqyzcqWYqVnxVCDnX0eJToPpgx+LDYuIhyx0W8ZacFjtrEotiQoUrIPRupZ8fg9NI80AyERo4UQjth5o4nkdJ9Sfpow+HE+EHDcWJBBWrJtwefYLLZgKb45g47GLQleioQjZmI2ZG0AlGG2EVP3AWXA80QnXHoxRpZpjyzLnBtUNkr1+fUrem1hCjjsFlxuH+0jBhgS7POadlMoauY3FS6+OMH1EvaGK3FbjvzNYE8D9zQMX9LamohZbJbRjRiyT2zwolJZ4HCGhm6bCHHd1ZcvPvKjPKOczKRM3H43l8aN872MGANG59vG25jzzZFuT1+yHL8i0AdtuGHqPQFHrMzE/WGLlOALTJ2JNuA1FwkJR8zECEese6qbGU+EzY32FNj1uPLYxFL2va5eBmcHsUm2JSODC2GVoKYoAzBIeeJQS2DAZYbpBlxGEpkH2JyzGZvFxtj8GD3TPbE3LJ5YrCGpZgMtUMuVSAmCg50NFJ239IUplFrMtcyOTM/TNaqUiS40LkIgSVKoJHjIjNF5420KxTW3NW/rkuX4FYGMWPuwi/EZg8zwmx5SZrshmVK6viGomJi15PNMXYYvPtexDlPpGJhrhUhwHbkpueQ4cpCRIy9PBNjze8rPq68bUhI7EoRsfW1uLSm2lBW/fmugXMoQjpgoSyypN92Ay3MRLk5J1iQ2Z28GQs3qpqLUWI6Yz+8LDtiONCN2oWcCr88YDeiS6NczWSkwhDaE6/PTyHQsrQa5JqLzfeeUUlQDsJxKqSDNDhU5yrp43ihZMXHvy9ukZKJNhN8vLzkuIhxxzbWMtuD2GcSJjpSULMhVEgxG64leioQjZnI2ZikZkMtjlyN/UXYlVN4ByHD+pzGxS2tEf9iO/B2rev3WEuGIGUBVKijUYa4ood8vBzcJyy1OEFqgEtF57WmfaGfGO0OKLYVq59Kle9V5aWQk25jz+Lk0YIzZfVKveRUTQ1noxcsSBaZWTsypVjb83jmlFNUAnB9S1OaWc8QAtgd6bg3TJ2bissSOEReTc16SbRY2B6pxFqM0vZTs5Gw8fg+to61xsjCOGGyti4SEI2ZyNmQr0YMr41dW9sIdH1V+nvuJ4eR5I+H0gHIR7y5c3UZpa0mgNLHXALLaJhXquDo8zfS8F4fdwqbC9GWfH3TETJkRC3yPB5rAM6etLREgIvNbc7diXSZLa7FIwRI4I5Qn2nyzSjkwmNsRq8ha9rnimmsbb8PlMUA/7UqwWEI9twbYFE67p4P94tvzty/7fDFPrKHLYBkxk61zELrv1ZU6sVuX3rZLkrQ2MtEGuOYiJeGImZxVZcQAat8DthSl+d9AqlCRMD43HnRMdxWu7qa9sSCdJKuFqTkvnaM6Hi7omQsbcGmuTaGQ0N5W6sS2zOIEUJerLE4XRy7i9XtjaVr8cZZDWj74vdCv/8U30v4wQbBPzACCHVkz7UjIymeSUaS1OaoSXiK1c5lyYIC8lDyK04qRkbk4Yoys0Yow0KawaaQJGZnitGLyUvKWff720pBaqe6DjX5/mCNmvoxYJCNawjF1n5iBZ/gtRsIRMzmrzog5MqH2UeXxuR+rbJW2iLLEKmcVOY6cVR3DbrVQU6xkxS7ouU+s/4KyOU/NUzaGJuKs2BBWLF8iBcrnnWpLZdY7u7KeSSMgSYbaFEaqmCgIH+ysd7JdAZVakwU+ANpXUCIlWBvRef0HKyPtDxNsKc7EapEYnp6nb0LnWfbRqzA3ATYHFK5MfMsInA32QkfW+2bqa66wDix2mBkOiLMYn4QjZnKEcuLQ7BDjc+Mre/GOjyk/G38BXvM0fUZbligQc6t03ScWXpZosgGXIiMWSWQewGqxBmesCKEIU2EQR2zOOxfM0Ee6KRSqmJcGpphx6zubmT0TcMRM2KvSELjmIimREpg7Oh+oqBhshvkpbW1ZBtEfFklZIkBKkpXNhYqzrfs+MdEfVrwDrEsLyBiNea+P5l5lj7Ezgr5MCDliHZMdTLp1vD9ZDfYwZ7tX/wGQSEg4YiYnzZ5GaXopAJfHV1ieWH03pBfB7Bhcfkl94zRCNUesRDhiOo7SB4U6zFU3P+P20tofWJwizIhBaFNoykihQfpVWkZb8Mpech25FKVFVrpXmOmgKNOBzy/T1KvjjYUsh2XEzOeIheaHZUX8GlNH5zOKILMMkEOzGnWILMvB9z9SRwxCGRjd94kFBzmb75pr6p3E7fOTm5ZEeU5kCs/ZjmzK0suU1yeCjron4YitAUSf2IrLEy1W2P4R5bFJyhNnPDM0jzYDsLsgOkdsm8iI6Vmww6RCHee7J/DLUJTpoMjpiPh1QcEOM0fnR9tgZlRbW5YgvD9MWkGWVmwKdd0nNtmDwzuBbLEp0XmTEencvnC25iozq/pd/QzPDsfGMC0p1X8ARLz3NslGbU5txK8zTG+miYU6RBa6vjxrRfdLc2eijVMSHAkJR2wNIPrEVizYAaHyxEsvmmKAXsNQAz7ZR0laCcXpxVEda1NROnarxPiMh+6xWZUsVJG5CRgOfOYmy4gFyxIjUG4LRzhil8cuM++bV9kqjUnNgewq5bGOZ6ycH1ZKpCLtDxOIPrEGHW8KJVEiVbAFklK1NUZl5jw+mvuUbORKHLFUe2pwRIG5N4X6dcTENbcxeyMOW+SBq/AZfn6/ToONnrmQQJEZy4GDvdBZK3qdqTPR4prrbQC/T1NT1CDhiK0BRJ/YijNiAIVblMiu3wNNv1LZsvhzZkCJoKxWLTGcZJuVTYU6FuzobQBkcFZAer7W1qhKQ9cYsPLFqTitmBxHDl7ZS8toSwws05jgAqXfSOFKe1UEwei8jvtVpF5lM+4vMZ9QR1PvJB6fTG5aEmXZkZVICdbEplDH0fkLQysvSwRFHTjFbmV63svVYZ3O8Ou/oOxPUvMga53W1qjO2cBaF8ncvnC25YfK8HVbsbNa8jZCUgZ4XDBk/FlpCUdsDbAxO1CaOHZldRfktg8rPy/+WkWrtEGt/jDBdj1vDoNlieYa5CzLMmeC5RorW5zCZ6yYOzqvz03hyOwIPdM9SEgrzohtC8w16hqdZWRan9lMKfC+yyZUTDwXVpa4khIpMHmZVEk9IMFEF0wNaG3NgoiMWKTiOAKb1UJdUMZeh8FGCCtL3G06Qarh6Xm6RmeRJIKzFCOlJqcGq2RleHaYgRl9fi9XjcUauO7QdSY6UhKO2BqgKrMKm2RjyjO1ugtyy3uVnx1v63ahiQS3zx2MxquREQPYEdgcntfjImVSoY7eiTmGpuaxWqRgn95KMLcjFvisu0/pcsaKyIisz1pPRlJk8ueCTIed9flpgNIjqDt8HqR+RbDBjI7YavrDBOEZMdNF55MzIL9GeazDTLTH7wnOcBNZkpWg+z4xUQ5sxrLEQMBxQ346mY6VqUGm2FKCQXhTr3UJRyyBEbBb7VQ6K4FV9ollVQTUiGRo/o2qtsWTppEm3H43OY4cqjKrVDmmrmvoRZ+QyRqYxeJUW5xBSpJ1xa8Xg51NuTgVbQfJCq5BmOzR2pqbWG1ZokDXfWIDjUjeOdzWVMhZr7U1qhN0xFZYDgxKVUaSJYkp9xSdU+aY/XMdOu4TE/2wGfYMKjMrV/z60Ay/cVXtUg0zC3Wssj9MsDZKgvV3za2UhCO2RhB9YiuWsBdsfb/ys+kZdQzSgPCyxJWW1izGxoJ0HHaL/mropwaUUhkkKK7X2hpVOdspauazVvX68BkrE/M6zKxEQ1Kq0tcJulyggo5Y3uocsXo9bwoDEtrjqdUgmWtpHZmep3N0BgiVY68Eu8VOba6i1mfOTaF+o/OiP2xb/jYsq/heimuuuW+Sea/OhBFmRpVhzmDKAeqr7Q8TmLokWHzeA03g0aFY2gow12qRYFHC+8RWhShPvPYOTPWrZFV8EY7YrgL1Imc2qyU4T+ycnsoTRblGfg04MrW1RWXOiijhKhenbEc25RnlgMkXKJ1tCn1+36pmGYUTXialuxK3wPs9mrZBY0PURzi+1flpOFNWNzBXbApNP9dIZ9/L1faHCcqyU8hOtePxyTT36WxotbjH5W6AlNWtB3rF55eDe4poM2JNI034TKAueB2ZpZBeCLIvpJppUBKO2BohKuVEgKxyKNuLUp74rHqGxQmf38fZQaVUTy2hDoGIEJ/XU5ReDLgsM1eU0O31Bwdor3ZxgpAjIDYppkKngh1tE23MeGdItYXkzFdKTXEGSVYLYzMeukZ1FgUNXHNjqWYsS1SuudVmoQG25m0FTJoRK9wK1mRlZIjI0OiEaIMfkiSFyhP1VhIcLtRhMtqGppme95KaFFJnXinrnetJsaXg8rjomOxQ10CtkaRQ/7vOgo4rJeGIrRHEUOe28Ta8fu/qDrLlfcrPpqfVMSqOtIy14PK4SLensyl7k6rHDg6a1ZOAgMiIlZqrgbmlf5J5rx9nip2qvLRVH0dEh0WpnKnQ6YwV8V5vy9uG1bLy3j5QRkbUlgRU3PQU+JgdgxGl7Hs8bXVOpp4J9qpE4YiJa655pBmP36OCVTrCag8N8NbRpnDSPUn7RDuw8rl94ehWsKPbnOschHqht5c5sVpW10phtVjZkquUqpsyAKLT6o+VknDE1gilGaWk2FJw+92rb5YOliceMVx54sm+k4CSDVvtJnAxxCJ1sW8St9ev6rFXhd8HPQGhDpMpSZ0NytavXEI7HNGj1DjcqL8St2jJ2wz2VHBPhQZ664BohToE9WWiFHg8WpPUI7ARkLOrcNtWF73WK7Ish0nXr778qyKjgoykDNx+9+pEo/SODjeFovS6LL2MHEfOqo9Tr0eRHFkOvdcmq/yA6PvDBObuE0tkxBIYCItkCWbFLo1eWt1Bssqh7BZAhovGUk880X8CgL1Fe1U/9rrcVJwpdtxeP639OqihH76kbMLtqZBfq7U1qhKtipSgJqeGJEsS4/PjdE11RW+YnrDaQgItOlqg1HLEdFkm1R1wxExYItU+7GJi1kOSzcLmotU7mZIkmXxTqD9HLFyoIxqEI3Z12MX4jDtas9RhrB1mR8GaBIWrz/bpFRF0jHatE9ecKTNiJYEZqaNXFeEWg5JwxNYQQrXq4ujF1R9k6/uUnwYqT/T6vZwZVPplbim6RfXjS5LEdhGl10O5lCjXKNmpbMpNhFBM3FkRXZTQbrVTk6vM/jk3dC5qu3RHMFJ4Sls7Aky5p7g6ofTOrFY0QCAcscbeCTw+HWSgIfg+yyXmc8TEPa2uJJMkW3RbBnPLaQeuub7z4NWHsxLsD1ulSqkgOy0pWAqum6xYIPhB0XawJWtri8q45r1cGlCCutGUA0Pofntp9BLzvvloTdMXqTmhUSE664leCQlHbA1Rm6M4Ys0jzas/iChP7DwK04MqWBV7Lo5cxOVxkZGUoXp/mEA4YroQ7Aj2h5lrUzjqctMxokho169CQvtGxObElJtCUZLarQ9HrHG4ERmZsvQyclNyozpWVW4aGQ4bcx5/cLOiKbIcfJ/N6Ig1BMuBo1elM3VGLKcaHFngm4dB7ZUhZVlWLQsNIYdAZGo0x8RCHee7J/DLUJqVQkGmI6pjFaUVkevIxSt7aRltUclCHVEWqHLqPqmtHVGQcMTWEMGM2MjF1ffFOMsCSjUytL6gnnEx5GS/coHuKdyjen+YYEdQOVEHgh0iUmiy/jBRiladn4YzdXUS2uEElRPNKNghFqeBJnC7tLUF9coSASwWKUw8QAfX2+jVYImUXLhVa2tUJ5pBzjciMmJt4224PNp/L1VFknRVntg93c3Y/Bh2i52anJqojyc+/7N6yYiJgKPJ1jkI7w/LivpY4SXBolTVVASDjglHLIEB2JC1AZvFxqR7kl5X7+oPVPMu5WfLc+oYFmOEIxaLskSBKJe6NDDFjHuVqpRq4HaForEmU5IKliWqEJmHkFPQOtZqvpKNzFLIKFZmrPQ2aG1NcEyAGo4YhCmV6mFTKDbdxTtMVyI15/FxsW8SUCcLnZeSR0laCTKyObNiOhodITbdNTk1JFmToj6euO82dI7h92sscOR1KyWgYMqMWINK/WECU5cEi6Bjzynw66RUfYUkHLE1RJI1KSjYEVV5Ys2jys+rr8O8DkqDlsDj9wT7w2Ih1CEozHRQmJmMX4bGnsmYnWdZehtA9iubcGepdnbEgLMqRuYBStJKyHHk4PV7o7se9Igk6aZk47oSqSh7VQTBjJgeSoHF+2uywAdAc98kHp9MTloS5TkpqhxTOOPm7M3UT0ZMbLqj7ckU1BRnkGyzMDnn5eqwxtnMgUalBDQlWykJNRGyLIfWOhUyYmDykuDCrWBLUWb4jaxyTq7GJByxNYaYKXFxJArBjvzNSoOkbx6uvKKSZbGhabiJWe8sWclZbMzeGNNz6WKws0n7w/x+WZVZRuFIkhR0DExdnqixI9Y11cX4/DhJliRVSqQgtEG5NDCFa17DDDSEDU83nyPWELYhjGZcRDg78pV5W6a85oRgx1ArzGkYkCOUhY5WMVFgt1qCvdCiOkEzwvvDVPpe6oXeiTmGpuaxWSTqSp2qHFMMU++c6mRiXgfl3GpitYeuO4OWJyYcsTWGEOyISjlRkgxTnihk6/cU7sEixfbrviOwSGmqKmXSTeHV4Wmm5rw47BZqopDQvhERnTd1yUb3SUVQQiNE5qM2txa7NfrePoCCTAfFTgd+GS70aLix8MxBf+C7Y7LgB1zviKlFeG+m6Wb4pReAswKQoa9BMzM8Pg8tI4owg1pZaAip1WqunGjiQc7Cya0tzsRhV6en3ZnsZF3mOsCkWTGD94klHLE1hsiINY80R7cIivLESy+Cz6OCZbFB9IfFsixREJxvpGlGTEQKzbVACaWu7aVZ2Kzq3bZEtNiU0fmSerDYYHoAJrSblaamUEc49XpQceu/AH4PpOZBdqV2dsQIseHeoaIjVpNTg91iZ2x+zHwz/EAXQ2Zbx1px+91kJWdRnlGu2nF1o5xoYsVEtfvDBGuiT0wnKsErJeGIrTE2Zm/EKlkZnRtlYGZg9Qcq2wNpBTA/AR1vq2egirh9bhoGG4DYCnUIRGli1+gsI9MaiD9M9sFkD0iW0KBDk3BWpUHON1KXW4eERK+rl+HZYVWPrTn2FCgKlCVpGCkMzjJS2RHbFYjOn9GyTEq8r2V7TFciNeZyc03FcRGCJGtSMCBo6j4xDTeFIvhRl1enWkkphDJiLf2T2olSzYzCyGXlsckqPwDV+8MEpu4TE4HnwSZwT2tryypIOGJrDIfNQXWW0twalUCBxQqbH1Ye67Q88cLwBeZ8c+Q4cliftT7m53Om2NlQkA5oVLoh+sPyayE5Pf7njyFnYxQlTE9KD343zCntq22kcM47R+toKwA78naoeuxd67IApZRHsxK3HvOWSDUEMvvVeeqMiwjH1IIdOigJjlXwo8gZKgnWbFSLuJflblAG+poIt9dPY6DUWji9ahGeETNdSXBmMTjLQfYjaVgSvFoSjtgaZEtOoDxxNEqlOFGe2PKcpj0oiyH6w/YW7VU1KrgUmpZLBfvDzFWuMeP20tqvNL6rvThBKFJo+k2hBjSPNuOVveSn5FOUVqTqsbeWOLFbJYan3XSNzqp67IgxaU8mwNlrgVlGKgc/wOQz/MJLgsc7NTFBbZXScEQwTLPyxG5lXacs9lUu8aalf5J5r5+sVDuVuamqHrsmpwabxcbo3Cg90z2qHlsXBO7Bkg4US1dKwhFbg4QPdo6KqjshKR2meqH3rAqWqcvxvuNAfMoSBWKR0iYjZs7+sPPdE/hlKHY6KMx0qH78oIrbsAk3hcJB6DsH3viXy4oN4ba8baoHQxx2K1tLFIEcTcoTp4dg/BoghfqCTMTpwHu6e536wY/6/HoALo1dYtarkRMdK+wpUBRwgLpOxP3043PjdE4pDqDIgqiJmCemmXKieE/LY9/3HW9ioVIqSLYmB8XazBx0lHqM1ydmGEdsbGyMxx9/HKfTidPp5PHHH2d8fHzR53s8Hv7sz/6Mbdu2kZaWRklJCZ/85Cfp7b1+kPHdd9+NJEnX/ffRj340xn+NtoQLdkSF3QEb7lce66w80eVxBW82+4v3x+28IiN2rms8vkMv/b6QM2yy6HysyhIFwhFrHG7E69dYCl1tsqsgNRd87pC6XxwR16DaJVIC8Z3QxBETC37eJnCoIzOtF3x+OSgasCsGWejC1EIKUgrwyT6ahptUP77mlN+q/OyOvyMmyhIrMytxJqv/vQxmxLrG41/i5veFAo4mzIiJtU7t/jCBWOtM74jpsEJrKQzjiH384x+noaGBQ4cOcejQIRoaGnj88ccXff7MzAxnzpzhL//yLzlz5gy/+tWvuHTpEu95z3tueu4TTzxBX19f8L//83/+Tyz/FM3ZnL0ZCYnB2cHoBQpEn9jlF6M3TEVO9Z/C6/dSml5KeaZ6qlHLsbkwgxS7lal5L21DcWwaHWpVmlST0iFfnVlNeqGhS9lki0is2lRnVZNhz2DWO8ulsUsxOYdmSFJow6JBeWKsFBMFwknQthTYXIEPCMxnc/tIT7axqVC9cRECSZJC5YlmzESLbI0GGTG1BznfSF2pE5tFYmhqnp7xOGczB5sD61wGFNTG99xxIDgrMwbBD4AdBYojJkTMTEXRdrDYkVxDpLqNJbxlCEesubmZQ4cO8eSTT7J//37279/Pv//7v/Pb3/6W1tbWBV/jdDo5fPgwH/nIR9i8eTP79u3jX//1Xzl9+jSdndfXbaemplJUVBT8z+k0V3TzRlLtqVQ5qwAVyhM3PABISrR9Qj91x0f7jgJwoORAXM9rs1rYJoZexrM8UUTnS3YqQiomQZZlzogoYYwyYhbJEtwUmnKBEo5CnDeF/a5+BmYGsEgWtuZujck5dgXK5pr7Jpl1+2JyjkXpMa8jdjrQH7aj3InVEpv+2mB0ftCM0flA8GOgEdwzcT11rLPQDruV2uJMgOC9OW6IDGPpLlOtc6ColLYPuwB1VUrDCS8JnvHE93sZc+wOKFbuKdmuKxobszJsWhsQCUePHsXpdHLrrbcGf7dv3z6cTidHjhxh8+bNER1nYmICSZLIysq67vc/+tGPeOqppygsLOThhx/mr/7qr8jIWDwKOD8/z/x8qN9iclIREvB4PHg82s7UEudfzo7N2Zu5OnGVpqEm9hdGUbqXlIm1dA+WnpP4Wp7Hv+vTqz+WihzpOQLALQW3qPqZRPL+bi/N5ET7KGeujfL+HeoKFCyGtfMEFsBXvBO/xt/BaLjx/e0dn2Voah6bRWJzfmrMrq9tudt4p/cdGgYa+PCGD8fkHFohFe/EBsjdJyO+P6hBQ38DABuzNmLHHpNz5qdaKcxIZmBqnjPXhrmlMk4qarIfW89pJMBTWA83vK9arwPRcrpjBID6MmfM/pYt2UqJ/Pmh87jd7oh6Ygzz/qYWYksvQprux9t1ErkiPgFBn98X6svM2baq9ymS97i+3MmFnglOtY/w8Jb81Rm7Cqydx5V1rmSPYde5xd7f0x1KFqc6L5VUe2y+47lJuRSkFDA4O8i5gXPsKTRXEMlSshtrzymyZ9p0cY+I1AZDOGL9/f0UFBTc9PuCggL6+/sjOsbc3Bx//ud/zsc//nEyMzODv//EJz5BVVUVRUVFNDY28uUvf5lz585x+PDhRY/1ta99jb/+67++6fcvvfQSqanqKt2slqXsB2BO+fFa82uUdJZEda6N/nVs4SSD7/yIE/03f07xZsI/QftkOxIS4+fHeb7xedXPsdT76x+RACtvNXXxvK1D9XMvxN0tr+METvdD3/Pq/73xRry/Z4eV97Ioxc9rL8eu/HXOo1wQx7qO8bwJ3r9wbL5ZHkFCmujized/Dvas5e8PKnBo9hAATpczpu9pkd3CABZ+cvg4w6Xx6Q3ImO3m3vkpvJZkXjjdgSxdP5g4Hu9vLHm7xQpIePsv8/zzsSnX9cgeLFgYnhvmP5/7T7ItkZdjGeH93Wsrp4R+Wl95iiuF43E5Z7+vn2nPNEkkcenIJa5Iq88MLPUeWwJr3GsXrrFburrqc6yU+1pfIx040QeDBr9P3/j+Pt9lASzkStMxvV8WeAsYZJCfv/NzBh2DMTuPFpSOWdkD5Liu6OIeMTMTWdZRU0fsK1/5yoIOTTgnTyp9DQtFy2RZjjiK9tGPfhS/38+3vvWt6/7tiSeeCD6uq6tj48aN7NmzhzNnzrBr18JKWF/+8pf54he/GPz/yclJysvLOXjw4HVOnhZ4PB4OHz7MAw88gN2++OyXwsFCXnj5BYZtwzz88MPRKfQMrIMnf0HRTAuPPHCPohqlIb9u+zUch625W/nggx9U9diRvL+7Juf47j+8Sd+sxN33HyQ1KcaXmXsaW4NSFrrz3U+wM6M4tueLITe+v6efa4HLndxTV8Ejj8SuJ2DaM80Pfv4Dxvxj3HLPLeSl5MXsXJrQ/00YbOKeDakcusay9wc1+OXhX8IQvGvXu3ik+pGYnafP2cG5Q5eYTS3ikUfiM8jccub70AKWilt4+F3vDv4+0vuvnhlxuRk6+joAT3zgfpwpsfs7fn7o51wcvUheXR4PVj647PON9P5ajrXDKyepTZtk0yOx+/6H84vLv4CTsLNoJ4/e++iqjhHJe1w/PssP/uktemct3H3/fbFf4wBmRrCfHQBgz/s+Bymx6aOKNYu9v7/4wWlghHft28ojt8Sur320ZZTGM43M587zyF3x+V7GjendzLdUc/aaWxf3CFEttxyaOmKf//znl1UorKys5Pz58wwMDNz0b0NDQxQWFi75eo/Hw0c+8hHa29t59dVXl3WUdu3ahd1u5/Lly4s6YsnJySQnJ9/0e7vdrvkHL1jOlh2FO7BJNoZmhxh2D1OSHkVWrHQHZJYiTfZg7z4Gmw6u/lgqcGJAqSM/UHogZp/HUu9vea6dYqeDvok5mgdm2FedGxMbgvQ0geyHzFLsORWxPVecEO9vQ2Bo6N7qvJheW9n2bDZkb+Dy2GUujl3kvsz7YnYuTai4FQabsPWdAfbF/F7l8XuCcwp3Fu2M6bn2VinXV0P3BDabLT4zA3sV5TZLxX4sC/xteloLVkpj7ygAGwrSycuMbYXHjoIdXBy9SONYI49ujNxxMMT7u04p+bf0nMJisynCOTGmcbQRgJ2F0V9zS73HFXk2ijId9E/GaY0DCJQ6k7sRe6b2lTfREv7++v0yDV3KWrenMjem3+3dRcqc0QvDF+J3v4wX2WV49nyaqcHndXGPiPT8mop15OXlUVNTs+R/DoeD/fv3MzExwYkToWbz48ePMzExwYEDi9deCyfs8uXLvPzyy+TmLn+zaGpqwuPxUFxs3KxCJKTYUqjJUdT1ohYokCTYFIhmaqye6Jf9HOs7BsRfqCMcIT8bl3liQr2t1HyDnJt6lYhSLGYZ3YippX3L9wEgxUlO+/LYZeZ8c2QkZbAuc11Mz6XJYOcuZUZhUKbcRIhRALtiJI4TjhAPMKVITvEOsCbBzDCMtcfllOJ9FPeyWCFJUvCeLIRdYo64d5WbT7b+0uAUU/Ne0pKs1BSpr1IaTm1OLUmWJMbmx4Lz5hJoiyFUE2tra3nooYd44oknOHbsGMeOHeOJJ57g0UcfvU6oo6amhqeffhoAr9fLhz70IU6dOsWPfvQjfD4f/f399Pf343a7AWhra+OrX/0qp06doqOjg+eff54Pf/jD7Ny5k9tuu02TvzWe1BfUA3B2UIVhzJseUn5eelHTGQ6to62MzY+RakuNmWpUJAQdsXioSplUva2haxyfX6bY6aA0K/blrmLz0jDUEPNzxZ3A5kXqO4fF74756YKy9XnbsUixXWbiPth5eghGA30xJrvmILSxjsX8sBsRa1DLaIupVdzoiv3oiJHZkeDGOh5rn1AsPRMvR0yovpaZb5CzuObqK7KwWWN7v7Rb7cFZsqYMgBgQQzhioCgbbtu2jYMHD3Lw4EG2b9/OD3/4w+ue09raysSEkt7t7u7mN7/5Dd3d3dTX11NcXBz878gRRVEvKSmJV155hQcffJDNmzfzR3/0Rxw8eJCXX34Zq9Vc0qgLIWZKqJIBqLoTbCkw0QWDUUriR8GR3oBaYtEt2C3apaXFHJCzXXFYpLoDAy5NlhE73aG8d/HIhkEoOt803ITHp73ikqpkV0J6IZLfQ9ZM7KPzsZbQvpG4DnYWkfn8WkjJiv354ojX5+d8oBw4HtddcVoxBanKYOfG4caYny/uBGf4xT4TLYIfG7I2kJkU+171YEascyz2g519Xug5ozw2YUYstNbFR/VVBEBMWf1hQAyhmgiQk5PDU089teRzwm8GlZWVy94cysvLeeONN1Sxz4iIjWfrWCsznhlS7VH0A9hTFGfs8otw6RAUxmZu0HKI+WH7SvZpcn7BtlJl/s7A5Dx9E7MUO2OU0ZnshalekCxQXB+bc2jE6cCmek+cHLF1mevISs5ifH6c5tFmTTOqqiNJygam+Vly4jBjRWTZxT0m1uyqyOZ773TExxELliWab0PY0j/FrMdHpsPG+vz0mJ9PkiR2FuzkxY4XaRhq4JZik72n5XvhGHGZ4Scy+bEuSxRsKc4k2WZhfMbD1WFXbL8vgxfB44LkTMivid15NOLUtfgGHU1d/WFADJMRS6A+RWlFFKcV45f9XBi+EP0BRZ/YpZeiP9YqmPXOcnZA2QDuL4liNpoKpITVep+5Nh67E4n+sIItkBz7jVO88PvlYMlLvKKEkiStiT6xHFds5MgFgzOD9Ez3XDcoO9bsqVQ2MBd7J5me98b2ZJ0BR6xC22BPLAiVSGVjidEg5xsRzroqJfJ6I3yw8/x0TE8lysxEtiPWJNks7AgMHo55n1hwkPNu0w1yHpyao3N0BkkKZfZjjVjnroxdYdod2+9lguVJOGJrHFWbpYUj1n0CXCPRH2+FnOw/idvvpiitiKrMqrif/0ZEj0VMo/Q95hTquDI0zeScl9QkK7XFsW1eDsfcjpgiLJHjuhLTPk6xod6UvYn0pPgEB4qdKZRmpeCXY9yX6Z2H3oDDYGKhjt1x6A8T7CxQRg6cGzqHX/bH7bxxwVkKmWWKqm3P6ZidxuPz0DTSBMQvCw1x7BPrNLE4TuC921yYQaYjPu0U+an5lKaXIiOrE4RPEBUJR2yNI/rEVElRO8ugsE5ZdK68HP3xVsib3W8CcGfpnbqQZBVR+lOxXKREf5jJRANOBzbT9eWxb14OJ1iyYcYm5uLtyNZkkr1TMBa7IazByHwcN4QQfr2Nxu4kfefBNw+puZBTHbvzaERQqGNdVtzOuSlnEym2FKbcU1wdj99w4LhREXAeRElrDGgZbWHeN09WclbMVUrDEaV0MV3jADoVJWQzZ6HjVZYoENUKifJE7Uk4Ymuc8KZNVaKRGsnYy7IcdMTuKr8rrudeDJERa+qZYM7jU/8EPm8oOl9qLkdMlHPGqz9MUJdXh1WyMjAzQN90X1zPHXNsycglSvZBimHPisiIiUxHvNgTDzntcNl6HQR71KR/Yo7usVksUkj1NR7YLXa25W0D4OyQCcsTKwJl8p1HY3YKsZmuz6+PaxBSjDi4MjjN+EyM1FgnemCiU+mDNlnAEUJOrAgkxQsRKDs3aMLqD4ORcMTWOJuyVY5GChn7Ky9DHJXnLo9fps/VR7I1mb1F+pC3LctOoTAzGa9fDiqRqcp1Dcybl3++gTgTmL+2uzI+/WGCVHtqcL6eGXtW5ID0syVGKm4znhlaRlsADRyxwHflzLUxvL4YlbiZWKhDZBJrizPJiFOJlMDUmWiRxek6Cf4YBOQImx9WEB+hDkFuejJVeWkAnI1VSXBXIBtWtA2S41emHg/mPD4aewIqpRXxXet2Fe4CFCfeF6PvZYLISDhiaxy7xU5dXh2gUoq6dLdStjM3EdNSjBsR2bBbi28lxRb7mVORED70MiblUuL9LdtjqgbmSTd0js7GtXk5HOFAnBk8E/dzxxo5IB4Qq8HOF4Yv4JN9ihBQenFMzrEYmwozyEi24XL7aOmfUv8EshzmiJmvROpUQEJ7b5yDHxC65swY/FCElDLBPQUDTaofXpZlzcqBIVT5EbNMdLAsUVsBrlhwoWcCj08mPyOZ8pz47ls2Zm0k3Z6Oy+Pi0lhsBZwSLE3CEUugrmCHxQobHlAeXzoU/fEiJLw/TE8EBTtisUh1mbOBuX1KKa2JZ/NyOCJSaMZNoVyqZMSk4VaYVf87GSxLzI9vNgzAapHYKQIfHTEIfIx3wvQAWOxQUq/+8TXmZOA9i3eJFIQyOV1TXQzPDsf9/DHFYg0NIRZOhYr0ufoYnB3EJtnYmhf/sTExDTZC6D0z2ToHoeDH7orsuPe1Wy3W4HVnxqCjkUg4YgnUH+4XZxn78bnxoO13lunLERPlUqevxWDopUkdsasBRyzezcsCEZ2/PHaZSfekJjbEjLQ8ppMLlcddJ1U/fLwltG9kbyzFA8T1VrxDmZtoIqbmPDT3Kd/1PXEaFxFOZlImG7I2ACbtWYlhn9jpAUWwqTa3VpNqEHGfPtc1gUftkuD5KUX6H0wt1KFF8ANgV4F5g45GIuGIJQjW53dMdjA2p8IGZv29IFlhuBVGY6+C9Xbv2/hlP5uyN8W9HGo5xNDLscDQS9WY7FMi9JLFdNL1HQFHTKvFKS8lj4qMCmRkU24KR9M2KQ9ULh32+X3B8uZ494cJdgvlxI4YBD7EJtpkgQ9Q+nv8MpTnpFDkdGhig3DeTbkpFE5E5zHVR0eIbIbYVMebjQXpOFPszIb1O6lG90lFhTlrHWSWqHtsjZFlOTQuQuOg49mBs+rfLxNETMIRS4Az2Um1U5FiVmURTMmCdQeUx3HIir3ZFShL1Fk2DGI49FL0+BRsBUemesfVmFm3j86Av6pFZF5g5p6V0TQl86B2mdSV8Su4PC5SbalszN6o6rEjpb48C5tFon9yjp7xWXUPfi3giK0zX6+KKOXcq4NrzpRy2qW7wWKDqV6Y6FL10GcHlHuUKKmONxaLxN5AAOSk2iXBJpatbx+eYdTlJtlmYWuJUxMb6vLqsFlsDM4O0jPdo4kNCRKOWIIAewoVWdjjfSpFyeMkY+/1e3m7920A7irTh2z9jYgo/ekOFR0xIT9uMvW2hu5x/LJEUWYyZdnalX+JTY0Za+dH0gMKmz2nlAHFKiGc1h35O7BZbKoddyWkJtnYWqIEJlQNfMyMwlCz8tiEogEnO0SJlHaOmOhVvjhykXmfet9LXZCUqpS0gqoBkPG5cdom2gDtMmIQEng5qeYaB6Z2xMSszO1lTpJs2mzFU2wpbMndApgz6GgUEo5YAgD2lyibi6N9KtWwbww4Yh1vK3XeMaJhsIEp9xRZyVnBWTR6Y7dQlepU0xEzZ3/YyaByW/ybl8MR0fnG4UbcvhjNx9GI6eRi5NQ88M5Bb4Nqx9VqftiN7AluClWMzosNYd4mSMtT77g6wOPzc7YrdN1pRXlGObmOXDx+DxeGLmhmR8yIQZ+YCBStd64ny5Gl2nFXyt4q5Zo71TGK369SiZvPA92nlMcmVCk9GQgU3VKlXfADQg68GYOORiHhiCUAYG/RXiyShfaJdvpd/dEfMG8jZFeBzw1XX4/+eIsg1BJvL70dq04l3HcF6r9VG3rpCdtAmywjdiowyFnLDSFAZWYl2cnZzPvmuThyUVNbVEeSkMXG5to7qh1Wa6EOwZ51oT4x1RDvkyi5NhFNvZPMefxkpdpZn5+umR2SJLG7UOl3NeWmMLxPTCXODCjv085CbYMfdSVOHHalF7ptaFqdg/ZfUOZkOpyQX6POMXWEuD/dUpWrqR3hfWIJtCHhiCUAlD6xrbmK9O2xPhUWCkkKDXeOkYy9LMu80vkKoN+yRICctCSq85Whl2fUyIr1NYDfA2kFkF0Z/fF0gtvr52xgkPNejZqXBZIkmbpPTBbR+WtHVDlev6ufXlcvFsnC9vztqhxztYhS4NaBKSZmVRoqL7IYFeZzxER/2J512Vgs2mWhgaAjJpQATYWoXhhsVm10hNZCHYIkm4Wd5cp1d0KtTHT4zD6Lubaq4/PQNTaLRYJdGszKDEcEztom2hifG9fUlrWKub7dCaJiX7ESsTvaq1LpxKaDys/LL6uuFAVwaewSnVOdJFuTuaPsDtWPrya71Rx6GVygblEcXpNwoWeCOY+fNJvM+oDjqiVm7hPzi4xY13Hw+6I+ntg41+TUkGbX9rMryHCwLjcVWVYp8OF2QV9APdOEQh2h+WHalkhByBE7O3gWr9+rsTUqk14AOesBWZXRETOeGZpHlL5F8b5piShPPNmukiMWDH6Yq/weoC2gDLy1xEmGBrMyw8lx5FDlrAJMKpRjABKOWIIgok/sWN8xdaRM190G9jSY7g9tZFTkpWuKIuNtJbdpvvlbjt1qlkt1mrM/7ERgAV+fKWvaHyYQUeaGwQb8ssrzcbSmsA6SMmB+MjSnJwpODSi9HEL0R2uE4qYqm8Luk+D3QmYZZFVEfzwdIcty8J6kdTkwwMbsjWQkZTDrnaVltEVrc9RHxT6xC8MX8MpeitKKKEnXXtr9FjUFO2Q5tM6ZUBynbVJZ37TuDxMk+sS0JeGIJQiyI38HKbYURudGuTR2KfoD2pKh+m7l8WV1ZexlWealDuWYBysPqnrsWCCizQ1d48x7o8hAyLJphTpOtI8AiiOmB2pya3BYHYzPj9Mx0aG1OepisYYizdei3xSKjJgeIvMAt1Yr19txNRwxE8vWtw+7GHG5SbJZqCvVRkI7HItkYXeBicsTg31i0V9zwf4wjcVxBDsrsrBaJHrGZ6MfHTF6VQngWpOgRB9/n5oIR2yvDrLQkOgT05qEI5YgSJI1KViOpUqfGISVJ6rriF0Zv0LHZAdJliRd94cJ1uenkZuWxLzXz4XuKIZejl6FmWFlgRJyyCbA5w9F5tdn6MMRs1vswX4nU0YKg31i0Ql2DM8O0z7RDujHEdsXaIA/3z3OrDvK0kvx/pgwMi/KEneUOUm26UPsSKxBpnTEhNhLz2nwROesnB4MBD8K9HHNpSWHRkdEnYnuUEbSULoH7NqNMYkFoy43/bPCEdM+Cw2ha65xpJE575zG1qw9Eo5YguvYX6y2jH3AEes+Ba5hdY5JqCzxQOkB0pO0U/qKFEmSgmUIUUXpxfyw4nqwO6I3TCc0900yNe8lLdlKqY6qTEWk0JybwtuUn51Ho+rhFJH5jdkbcSZrn1UBKM9JodjpwOOTo+sT87pDEtri/TIRx68q96JbNVZuCydcOdF0JcE51ZBRrKgJd6++T8zj93B+6Dyg3SDnhRAZnqgFO0Two9J819zpgDLwhvw0ctOTtTUmQFl6Gfkp+Xj9Xi4Mm3B0hM5JOGIJrkMIdpzuP63O/KTMEijaBshw5eXojxfgcMdhAA6u039ZokA4YieicsQCmUqTydaL92RPRTYaC7ddx54ipefp1MApdfom9UTpLrAmg2sIRq6s+jB66w+DGwIfV0dWf6C+c+CdhZQcyN+sknX6QJZljgXem33V+nHEanNrSbGlMDE/Qdt4m9bmqIskhRz6jtVnoltGWpj1zpKZlMn6rPUqGRc9whE7FY0jJsuh96bydhWs0hdiftgenWTDQLlfivv3qf5TGluz9kg4YgmuY1P2JnIducz55jg3pJLAhsiKXXpRlcO1jbfRNtGGzWLj7vK7VTlmPBBR51Mdo3h9q4z0Crlxk80zEo6YXko1BDvyd2Cz2Oh39dM93a21OepiS4aygPMUhYy93vrDBOJ6iyoD3Rl4Xyr2m0qhFKBrdJbeiTlsFold67K0NieI3WJnR75Sdm3KTLTI8ojyu1UQLltvkfSzjRP370sD04y5VhnIHb8Gk91gsUOZuQKOAKcCjpjWI1puRAQdTw5Er+iZYGXo5wpOoAskSWJficoy9hsfVH62vQK+6CWJg2WJJQfISMqI+njxYnNRBpkOGy63j4t9kys/gGsYhgMiKibqV5FlOdirojdHLMWWwra8bYBJI4VRzhObmJ/g8thlQIeOWECw42zXOHOeVfaJmVio41hAHGdHeRapSTaNrbkeU88TqwyMWuk+CZ7V9eOILLTWg5xvJDc9OTh65ORqs2LB/rBdkJSqkmX6YHreS1Ovsvbrba3bW7QXgHOD55j3zWtszdoi4YgluAlRnnikV51hr5TtUUp75iag+0TUhzt8zXhliQBWixSqoV9NlF4obeXXQKo+1JbUoG1IUW5LtlmoCzR76wlRsnGy34SRQpFZ7VzdtX5m4AwyMlXOKvJS8lQ0LHqq89LIS0/G7fVzLjAofEX4/aYe5CzKEm/ViYR2OOGOmOlKgnM3QFoB+OYV0Y4V4vP7gg7q3sK9alsXNbdEm4kWZYkm7Mk8fW0Mvwy5yTLFTn31eFdmVpLryMXtd3NhKNEnFk8SjliCm7it5DYkJJpGmuh39Ud/QIsVNtyvPI6yPPHqxFUuj102XFmiICrBDhGdN1E2DOB4IDK/qyKbJJv+bkkiUmjKPrHyW0CywHgnTKy89FJE5vWWDQMlux+VjP1AI8yNK7MQi7era5wOEEIdeuoPE2zL24bdYmdodojuKZOVBEtSqDxxFYqlrWOtTLmnSLOnUZtbq7Jx0bN/vfJ9Otq2yt7Ma4GMmAmFOsSIlmqdjGgJR5Kk4FqXKE+ML/rb9STQnPzUfOoL6gF4pfMVdQ66UR0Z+2fbngWUskS9KLSthFsDm56THaP4/Su8GYushckihWLBFptmvbEjfwc2yUafq4+e6R6tzVGX5IzQGIRViAeIyLyehDrCuTUagZyOt5Sf6w6A1a6iVdrTNTpDz/gsNosUHDavJxw2R6gkeMCEJcFChEJ8x1aAyMzvLtyNzaKvklKAfYH7eHP/JOMzK+wTG+9SgkKS1XRzMgFOtutrRMuNBIOOZizD1zEJRyzBgjyw7gGA4NDkqNlwnxJ5H7yo3GxXgc/v4zdXfgPA+za8Tx274szWkkxSk6yMz3i4NDgV+Qvnp6BPkSs2U7+K3y8HHbHbNuirtE2Qak+lLq8OMGl5YtWdys/2N1f0smn3NM2jzYA+M2IQEuw4fW0Mz0oFctoDm+SqO1S2SntEhnBbmZO0ZP1t5iH0nTKlI7Yu4Ih1nVRGJKwAcQ/SY1kiQEGGgw0F6cgyHLu6wgCIyBCW1CtBIhMx6/bRECiRXq/DjBiEBDvODZ1TRzU7QUQkHLEEC3J/hVJKeHbwLMOzKsz/Ss0JKSCtMit2pPcIg7ODZCVncXfZ3dHbpAF2qyUYgV5RlL7rBMg+cFaAsyxG1sWfS4NTjLjcpNit7CjL0tqcRQkvTzQdlQFHrGNljljDUAN+2U9ZehlFaUUxMCx6Nhakk51qZ9bj4/xKBqn7fSEBk0rzOWJ6lK2/kaCKW/9J85UE52+G1DxlNEJv5MPivX5vqD+sWJ+OGMD+wPfq2EpHRwihDpNVfYASDHL7/BRlJpOvr/awIFWZVeQ6cpn3zSfmicWRhCOWYEGK04vZlrcNGZlXrqlUnrgpuvLEZ648A8C7qt+F3cClQrdUrqJvpdOc6m1HrigL9d6qHF32hwnCN4Wmo2IfWGxKSdBYR8QvE+Ures2GAVgs4YPUV7Ap7DsH8xOQ7AyVbpoI8V7oUahDsLNgJzaLUhJs6j6xFcjYt4y2MO2ZJsOeQU12TYyMi559q3XErpl3ftiRNiWgvb86R7eTMCRJMvdap1P0u/NJoDmiPFGoFEaN6BO7+gZ4Zlf00vG5cV7reg0wblmiQPSJnWgfjTzSa1KhjiOBssQD6/UbmQeoz683cZ9YOpQGnKkVlCeKhVos3HolOE9sJWVS4n1Yd0ARGzIRPeOzdI3OYrVI7KnUryOWYksJzhM73n9cY2tigChPXIFgR3h/mFXH30vRJ9bSP8XIdIRS6JO9MHpVaWGo2BdD67RBrHX7dNoLLRAlr4k+sfiRcMQSLMr965TyxJMDJxmdi2IoqqCwDjJLlXKMFQ6zfK79OTx+DzU5NdTk6DcSGAnby5wk2SwMTc3TPuxa/gXeeegJ3BRNVLLh9fk5ftUYjliqPZWteVsBk0YKg31ikYkHTLmnaBxpBODWIn031QsRmFMdo5H3iQkRBfG+mAhxzW0rdZKu0/4wgfhuneiLfuyJ7hAZsc7j4PNE9JIT/cr7IEql9UpuejKbC5Uer4grP4RYUNE2cBhPiGspJuc8nO8eB2CfjrPQEPpuNQw1JPrE4kTCEUuwKOUZ5dTm1OKX/bzW+Vr0B5Qk2Khk2VZanvjrK78GjJ8NA3DYrdSXZwERNjP3NoB3TukpyNsYU9viSVPvJFPzXjIdNraW6H/hDUr7mtoRexMiyNKe6j+FX/azLnMdxenFMTYuOmqLMslOteNy+yKbJ+bzhDLQJhTqCM4P03lkHuCWYqWv+Hj/cRP2idUq8zU9Lug9u+zTPX4PZwaUfjLxvuiZFcvYB1VKzVeWeLJ9FL8MlbmplGSlaG3OklQ5q8hx5CT6xOJIwhFLsCTqlyc+qPy89GJEGz6A1tFWmkebsVvsvKvqXerYoTGimVnUjS+JKF2p2Idui8tXQahUIxerRf9/l5BoN2XJRtktYE2G6X4YubLs00WpmN6zYaD0iR1YryhyvnMlgk1h71llc5ySAwVbY2xd/Dmm4/lhN7I9bzsOq4PRuVHaxtu0NkddLJZQL1T7G8s+vXmkmRnvDJlJmWzK3hRj46JHfL+ORtondvV15Wf13TGxR0vEWrd/vT6VgcORJMnca50OSThiCZZElCce7zvOxPwKVMcWo/ousCbB+DUYvhTRS4RIx93ld5PlyIreBh1w+0blhnykbWT5eWJBoY4DMbYqvggnVO9liYKdBTuxSTZ6Xb3mEw+wO5ThzhDRpvB4X8ARK9a/IwZwYIPyHXsnksCH6A+rvE3ZLJuIrtEZOkdnsFok9uq4P0xgt9rZVbgLMGmfmHA6ri5/zYmyxD2Fe7BI+v9e7guIUlwZnGZoapk+sdF2ZU9gsZlunYNQVtAoa52o/hDfuQSxRf9XcwJNqXJWsSFrA17Zq85w56S0UBTw0ovLPt3lcZmqLFGwoyyL1CQroy43Lf1LzBPz+5QeAjCVUMe818fJDiUyf0Cn88NuJNWeyrZ8Zcjssb5jGlsTAyLsExueHebK+BUkJG4p0n+JFMDtge/Y2c4xZtzepZ8sSqQqzdcf9vYVxRHdWZ6l+/4wgfiOCeffVAhHrOs4uJfuFxbZCSOUJQJkpSZRW5QJRKCeKLJhZbco4kEmYszl5mLfJGCMLDSEAmxnB88y612ZsFqClZNwxBIsy6PVjwLw09afqlOnL8oTI+gT++WlXzLlmaIys5LbS81TO55kswRltZcsTxxoVGS0k9KhaHucrIs9DZ3jzHn85KUnsbHAOAvvvmJFzeto71GNLYkBwhHreAv8i4taiA1xTU6NYTLUFTmplGal4PHJS8/v886HAh8mFOoQjpjIyBsBsSk81X8Kn9+nsTUqk1MNznLwuUOVDwvg8Xs4M6j0h4myMSMQ7BNbzhETWfjqu2JsUfwRTuimwnTyM5I1tiYyKjMrKUoruq4vMUHsSDhiCZbl/Rvfj91i5+LIRRqHG6M/oJgn1nkU5hYvd/T4PTzV/BQAn9r6KUOUY6wEEaUXm6MFCZfRthojgh0J4TXzkoH63vaXKFnJ4/3H8csRKvAZhZJdYE+DmREYal70aUYrSwSl7+G2DaIvc4lNYfcpRdU1rUAZumsi/H6ZI8IRM0gWGhSHP8OewZRnitaxVq3NURdJCjkfIiu0AI3Djcx6Z8lKzmJjtnEEm4KDnZe65vz+UGmmifvDDhigP0wgSVIw6GjK6g+dYa6dbYKYkOPI4aHKhwD4SetPVDhgNeRuBL8X2hZXY3yp4yX62pNXSwAAV5RJREFUXH3kOHJ49/p3R39enSFuzCfaR3F7F9nUt5tTRttoNfOCurw60uxpTMxP0Dy6uLNiSGxJofk9i8wTk2XZkI4YwG0i8HF5icBHsCzxdlMJ4wBc7JtkbMZDerKNHQHVViNgs9jYXaTMuTsxYMKelep7lJ9LOGJHeo8ASkbeSAHJW6pzsFokrg676BlfpMRt4ALMjipVH2KeoYkIDnI22Fq3v1gJOpqy+kNnGOeKTqApH635KACH2g8xNjcW/QHFcOdFyhNlWeYHTT8A4OM1HyfZaoyU/kqoKcogNy2JGbePc4EZI9fh88I1ZQGm0jwy2tPzXs52Kd8hozlidos9OPDSlAtUuIz9AnRPddPr6sVmsbGrYFccDYseEfi42DfJqGuR+TgiMGSywAeEMu/7qnOwW4219At1zpMDJh4d0X8BXAsHCYQjdqDEWEIWmQ57cFTLW5eGFn6ScEArbwerPS52xYuByTnahlxIEuyrMtZaJwJtrWOtDM9GIHKUYNUY626cQDO25W1jS+4W3H43T195OvoDbgqTsV+g7v94/3GaR5tJsaXw2ObHoj+fDrFYpGCUbMEofV8DuKeU4ZZF2+JrXAw52jaCxyezLjeVdblpWpuzYvaVmLhkQ5RJtb+14JDZY/3K37w9bzup9tR4WhY1+RnJ1BQpQ2YX7Mucm4DuwEZ/w31xtCw+vBNwxG4zUFmiQAhUnB08i1deRmzFaKQXQGGd8ngBxdKJ+YlgS4AojTYSdwT6Ed9aLBMtHLEq8/WHicqPuhInzlRjOZm5KblszlbKs005UF1HJByxBBEhSRIf3axkxX7W+rPom6bXHYBkJ8wMQ8/pm/75+03fBxSlRKMIAqwGsSlacGMYlNG+AyzWOFoVW964NAjAnRvzNbZkdYiSjbMDZ5nzzmlsjcoU7YDUXCUA0H1z9kGUJYr+AaOx5Dyx9jdB9ill01kVcbYstsx5fEGREiP1hwk2ZG0gx5HDnG+OLl+X1uaoT1DG/vWb/ulE/wn8sp8qZxVFaUVxNUsN7tyk3OffvjKM78ZRLd750PB0E/aHvRnIAorxGUZDOP5H+0xY/aEjEo5Ygoh5qOohMpMy6Znu4e2et6M7mNUOG5UZZbQ+f90/tY628k7PO1gkC49veTy68+ickKz2OK75GyK9wX4V85QlyrLMG4HF6a5NxnTEqpxVFKQW4Pa7zacoZbGEelauXD+uwi/7OdmvOGdG6w8ThAQ7Fgh8iL93/b1xtCg+nL42xrzXT2FmMhsMpFIqsEiWoPN/2XNZY2tiwBKOmFHLEgXbS51kOmxMzHq40HODOFfXiZA4TkGtNgbGCL9f5s1AFtCoa124YIcqitkJFiThiCWImBRbCu/f8H4Aftz64+gPuOlh5WfroeCvZFnmH07+AwAH1x2kPKM8+vPomPKcVMpzUvD6b5DV9rqhM1D6VmUeR6xjZIau0VnsVslwzcsCSZKCWTFTlieKsry26x2xy2OXGZ0bJcWWwrY8Y5bK3lqdi9UicW1khq7RmdA/yHLo7zVhWeLbYWWJRlIpDUeML7nivaKxJTGgYj9Y7DDeqQw3DiDLcrAX1aiOmM1qCVZ+3NQnJhzP6rtNJ47T3D/J8PQ8qUlW9qzT//D0hdhVuAu7xU6/q5+OyQ6tzTEtCUcswYp4bPNjWCQL7/S8E302YOP9IFkVqezA4vNq56sc7z9OkiWJP971xypYrH9uC5ZLhUXpe06DZwZS8yDfPJHCN1qVssS9lTmkGWSg7EKIPjFTlmyIjFBvA7hCJXwiC763aC92gzbVpyfbguIB111vo1eVTbDFDutu08a4GCJ6UI1YligQZVK9vl5GZpeZS2U0ktOhPDCoOSwr1jXVRc90DzaLzVDzw27kjkAZ+k19YkFHzHz9YaLy48D6XJJsxtxqp9hS2FmwEzCpOJVOMOa3I4FmlGeW84GNHwDg6ye/Ht0spZRspVcM4NIh5n3z/MMpJRv2qa2foiyjLFpzDcFtC80TC5fRtpjnMhWL050GLdUQiJKNltEW820KM4oC4gEyXA2NlxCOmNEHqwvxgDfCo/Ntryo/K/Ypm2ITMeZy09irlIQZ2RHLS8mjJrsGgKP9JtwUBssTQ9ecKEvcWbDTcOI44Yhr7kznGFNzARGg2XHoDQRzTSjU8aZJ1joRADFl9YdOMM8OL0Hc+Hz950mzp3Fx5CK/aftNdAfbpMwno/UFftD0A3qmeyhILeCz2z4bvaEG4cD6XCQJWvqn6J8IiD8IoQ4TlSXOeXwcvao4LUatmRfkpeSxKXsToDTTmw6RFbvyMgDT7mkaBhsA4zti92wuAJQskccXCCSZuD/s6NURZBk2FaZTkOnQ2pyoMPVso/A+MZ/SL2z0/jBBeU4qVXlpeP0yx64GSvCvvgayH/I2QZa5WhCm572cvqaMaDH6WieuuZP9J/H6TaZYqhMSjliCFZObksvvb/99AP7nmf/JjGdmmVcswWalT6y/+yhPnv93AL64+4uGjv6tlNz0ZHaUZQEBRUHPnNLEDKaKFJ7qGGPO46cgTEbcyIismNgsmYpgn9irIMsc6zuGV/ZSmVlp+L7NbaVOctOSmBKbJa87lIE2YX/Y64Fy4Ns3GHtDCCGH5Gj/0eiqMfRI6W6lSiQwRsHj9wSDPEaUrb+RkIx9IBN9+bDyU8wUNRFGH9ESTk1ODc5kJ9OeaS4MX9DaHFOScMQSrIpP1H6C8oxyhmeHefLCk6s/UO565LyN/GNWBrO+OXYW7OSRqkfUM9QgiCj9ay1D0H0CfPOQXgS5GzS2TD2EbP1dm/INKxgQzm0lSi/ROz3vmG9TWLEf7KkwPQADjaYpSwRlfp8oF3q9NXC9uaeVfsxCY4qQLIbfL/Naq7LxvafG+I7Y9rztJJPM+Pw4zSPNWpujLhYrbAgoCV9+iQtDF3B5XGQlZ1GbY/w+YdEn9ualIfD7Te2IBcsSDTqiJRyrxRrMir3V/ZbG1piThCOWYFUkWZP40z1/CsAPmn5A91T3qo/1w5KNvJiehgX4s1v+zBSb9JUiNklvXxnG1ybKEu80lZKUWfrDBHuK9pBiS2FodojmUZNtCm3JSn8iIF9+mbd6lAXYDI4YwN2bhSM2GOoPW3+vqfoxAS72TTI0pSi33VJlTOW2cOwWO9X2aoDoR6joEeGUXH4pKAS0r3gfFsn438t91TnYLBIdIzP0tR4H1yAkpStBH5Nh9BEtN3Jn2Z0AvNn9psaWmBPjX90JNOPe8nu5tehW3H43X3jtC0y5p1Z8jLd73uafppoA+NLEHFuzNqttpiGoK3GSl57E9LyXmRalL4eqO7U1SkV6x2e5NDCNRTK2YEA4SdakYKTQlAvUeqVM7/LVQwzODOKwOthTZFzltnDu3JiPJdCX6W4NROZN2B/2aosoS8wj2WaOofAbbRsBk5YEr78PkJQs9DUlQGD0/jBBhsPOropsAAZPP6v8svpusCVpZ1QM6Bh20Tk6Y+gRLTdyW+ltSEi0jrXS7+rX2hzTYRhHbGxsjMcffxyn04nT6eTxxx9nfHx8ydd8+tOfRpKk6/7bt2/fdc+Zn5/nD//wD8nLyyMtLY33vOc9dHevPruzlpAkia8c+Aq5jlxax1r5k9f+BLfPHfHrr05c5f994//Fj8wHZjz8zuggdJpwcY0Ai0Xirk0FZDJN2vA55Zcm2hiKUo0d5Vlkp5ln4RWRQlOWbAT6pd4eawUU2fpka7KWFqlGdloS9eVZ5DCJfTDQ97D+Hm2NigHCEbunpkBjS9RDOGLnhs4x6Z7U2BqVScuFsr0MWS00jl8CzJOFBrgrkIlO7wpkoc1YlhjogduzztgjWsLJceSwLV8p2xbVEQnUwzCO2Mc//nEaGho4dOgQhw4doqGhgccff3zZ1z300EP09fUF/3v++eev+/cvfOELPP300/zkJz/h7bffZnp6mkcffRSfzxerP8VUlGWU8a37v0WqLZXj/cf5i7f/IqJ+meHZYf7o1T9i2jPNroJd/EXR3UgAzb+Nuc165d6aAm63NGLBD/k14CzV2iTVeLk51B9mJu4oU1QtG4cbzSdjn7sBnBW87VBmhplpQwhw9+YC7rWeRUKGou2KbL+JGJme51z3OBDqQTUD2dZsKjMr8ck+jvcd19oc9dl4kDdTUgCoy60jP9U898z7agvIZpKquRblFxsf0NagGPBGq7lK8AV3libKE2OFIRyx5uZmDh06xJNPPsn+/fvZv38///7v/85vf/tbWltbl3xtcnIyRUVFwf9yckJ18hMTE3znO9/hn/7pn7j//vvZuXMnTz31FBcuXODll1+O9Z9lGrbkbuFf7vkXbJKNFzpe4H8c+x9Mu6cXfK4syzx39Tne9+v3cW3yGsVpxfzz3f+MvfY9yhNangNZjqP1+uH2jXncZVWi81Ol5ilLnHX7ePuKsjgd3GKuzW5BagG1ObXIyObrWZEkpjfcy1mHkgW7o9Q8oxRA6RO736LMMfJtfFhja9TnjUtDyDJsKc6kyGls2fobESXB7/S8o7ElMWDjA7yRqjhid5ks+LG5MIP3ZbRgkWSmnJshs0Rrk1QlfETLnZvMUYIvENUfx/uOM++b19gac2GIvOnRo0dxOp3ceuutwd/t27cPp9PJkSNH2Lx58b6i119/nYKCArKysrjrrrv427/9WwoKlOjg6dOn8Xg8HDwYSo+XlJRQV1fHkSNHePDBBxc85vz8PPPzoS/i5KRSHuHxePB4PFH9rdEizh9vO/bm7+Ur+77CXxz9C35+6ee82PEiv1PzO3x080dJt6cz652lz9XH/z73v3mtWxlYWZNdw9/d9ndk2jLxVNyOzZ6GNNmNp+sUFNfH1f5IieX7m2qVuc9+AfxwVNrOPRp/l9TiteZB5jx+SrMcbMhzLPneafX9jYbbS26nebSZN7re4JF1+lb8XOn7eyS3HO+oxDqvnyJHgaE+l+XYnG1hQyDwcS51H9tU+Nv09P195eIAAHdtytWFPWog/o59Bfv4ceuPeaPrDebd86YQsxDMZ6/naMARu520uH92sf4Ovy+tCcbhpH0Pd5jkeyl4s3WIGbePosxkNualLPge6ukesRLWZ6wnPyWfodkhjnUf023vop7e30htMIQj1t/fH3SewikoKKC/f/HGwYcffpgPf/jDrFu3jvb2dv7yL/+Se++9l9OnT5OcnEx/fz9JSUlkZ2df97rCwsIlj/u1r32Nv/7rv77p9y+99BKpqfqYf3X48GFNzvvx1I/z0txLDLuH+db5b/Hk+SeRkJgn5LhasXK3427u9N/JxbcvcpGLAOxN20LJ+EmuPvdNWko+pIn9kRKL9zd9rof7/MPMy3a+dTGVWZ5f/kUG4EdXLICF9Y4ZXnjhhYheo9X3dzVYvMom8M3ON3n2uWexSvoXRYj0/f21S3FUbne5OPKLbzGeVh1Ls+JKwcQ59jNHn5zD/zoxzXsH1bvetP7++mR4tdkKSCSPXOb55y9rao/ajJ0fI5lkhueG+fdn/51ym7Fn24XT6mllTpIo9HpJPvIcz3dmL/+iGBCT77Ds54FJRQ3yPwaqmXjueSzmEQbmJ23KWrchZXbZtU7re8RqqPBVMMQQTx15ivHUca3NWRI9vL8zM5HN2NXUEfvKV76yoEMTzsmTJwEWlDSXZXlJqfPHHnss+Liuro49e/awbt06nnvuOT7wgQ8s+rrljvvlL3+ZL37xi8H/n5ycpLy8nIMHD5KZmbnk3xNrPB4Phw8f5oEHHsBut8f9/I/wCH/i/xMOdx7m/zb+XzomO4L/5rA6qM2p5c/2/Bmbsjfd9Fqp0QW/PskmXyvVj+gzsxDL99dy4tvQDMf9NbTMpHLvA/fgsOt/U78UXp+fr5x7A/DwxMO3sK96aQltrb+/q8Ev+/nZr37G2PwYRXuK2Fu4V2uTFmUl769f9vONZ74BHrh9dpb9+ZP479bndbkaLC8omflXfDvp8mXyyCO3RX1MvXx/T3aMMXvsJNmpdj734QewmmS3K97fhw8+zDvH3+Fw52HcFW4eqTfP9/LcyXNwGe6amWWD3EZlnNfCWH6HpZ5T2BqmmZRTecuziT/ccYDtZU5Vz6EVfr/M//iHNwA3v/fQHu5YRB1YL/eI1ZDSlcLpt07TndTNww8/rMtRQ3p6f0W13HJo6oh9/vOf56Mf/eiSz6msrOT8+fMMDAzc9G9DQ0MUFhZGfL7i4mLWrVvH5ctKdLCoqAi3283Y2Nh1WbHBwUEOHFg87ZqcnExy8s3qYXa7XfMPXqClLXbsvHvju3lk/SO0jrWSYkshLyWPdHv60hduzSPwrA1puBX7xDXI0+8w45i8v+2vA9CQtIs5l59TXZOGb7I/0zXC2IwHZ4qd/RvysVkjKyHS07UUCXeU3cFv2n7Dkb4jHCjTZ8lGOJG8v+eGzjE8O0yaJYlbZuewXn4R6wN/FScLY4wsw+UXAXhN3s3lQRe9k27W5aapcnitv79vXhkFFHEcR7J5VEoFdrud+9bdx+HOw7zR8wZf3PvF5V9kAGRZ5q1eRZXurlk30kwb9slOyF0fd1ti8h2++goAlzJuwTdv5Y3LI+yuMkcv1dnOMYam3WQk27h9YyF229Jrndb3iNVwe/nt2C12uqe76Z7tptqp3woJPby/kZ5f08LqvLw8ampqlvzP4XCwf/9+JiYmOHHiRPC1x48fZ2JiYkmH6UZGRkbo6uqiuLgYgN27d2O3269LYfb19dHY2Lii4yZYGKvFypbcLVQ5q8hIylg+epKSFZqd1fJszO3TFZ456FAaz+XA/KaXL94cfDAahwN/w301BRE7YUZEqCe+2WMeRalXOpVN052lt5MkWWGwCcY6tDVKLfrOwVQv2FPxVSqf3YtN5pmP85oJZetv5I6yO7BJNq5OXOXa5DWtzVEFMafJYXVwS0G98stLhzS1SVVangPAv0HpyxdqumZArHV3bc4naRknzKik2lPZU6jMkjTlyBaNMMS3pba2loceeognnniCY8eOcezYMZ544gkeffTR64Q6ampqePrppwGYnp7mS1/6EkePHqWjo4PXX3+dd7/73eTl5fH+978fAKfTyWc+8xn+9E//lFdeeYWzZ8/yO7/zO2zbto37779fk791zVPzqPJzrcnYdx4F7yxkFLNzt6II9mJTPz6/cRUkZVnmcLOyOD2wJfLMtRE5UHIAm2SjfaKdrskurc2JGlmWebVTmfVzb/XDUKF8J2mNrMdP94jN7fp7ua+uAoAXm4wf+AC4NuKidWAKq0Uy3biIcDKTMoMDxl/rfE1ja9Th9a7XAdhXsg9HTUBJuNkkQcnhKzB4ESw2Ntz+ISQJLvZN0js+q7VlqiAcMbOvdUI9UXxXE0SPIRwxgB/96Eds27aNgwcPcvDgQbZv384Pf/jD657T2trKxMQEAFarlQsXLvDe976XTZs28alPfYpNmzZx9OhRMjIygq/5l3/5F973vvfxkY98hNtuu43U1FSeffZZrFZj9+YYlpp3ARL0nILJXq2tiR9tSvaB9feyf0MezhQ7w9NuTnWMamtXFFwamObayAxJNovpZqrcSGZSJrsLdwOhTJKRaRtv49rkNZIsSYpsfU2gT6XVHAIywb9j88M8EBipcPraGIOTcxoapQ4vNCqZvX3VOWSlmq8sMZx7ypUh3K91mcMREzOa7i67G2oDQcnOYzBlgiBB82+Un1V3kpNXyK4KpR3klRbjZ8U6hl1cHpzGZpG42+DtBMtxd/ndAJwZPGO+2ZkaYRhHLCcnh6eeeorJyUkmJyd56qmnyMrKuu45sizz6U9/GoCUlBRefPFFBgcHcbvdXLt2je9///uUl1+vruRwOPjXf/1XRkZGmJmZ4dlnn73pOQniSEYRlAXEDgJlDGuCK0r2gfX3YrdauL9WiaqJTZUROXxRsf32DXmkJRtCoDUqHlinDCc9fE17taZoEc7kvpJ9pNnTYHNgzlbHOzA7pqFlKjDRo5QmIsHGBylyOthZkQXAiyYoBxb3jIfrijW2JPYIR+zs4FnDbwqHZ4e5MKyolN5Zdic4y6B0NyBDiwkqRERmr/bdgDLcGeCVZuNfcyIbdmt1Ds4UY/V9rZSyjDK25G7BL/t5tetVrc0xBYZxxBKsIUQk0CwlGcsx2af03yDB+nsBeGSbEqU/1NiP36DliWulVENw37r7kJA4P3yevuk+rc2JCuGI3Veh9CuSUw35NSD74LLBh92LssSyvZCuZGof3Kpcby8ZvE+sZ3yWc13jSBIc3Gr+6644vTg4UF1kk4yKKAWuy60jPzVQQVAryhN/o5FVKjHeBb1nACnYfiCCjUfaRpie92poXPQE17pa819zEBZ07DB+0FEPJByxBPpDLD4db8H0kLa2xAOxMSzdDamKvPvtG/NIT7bRPzlHQ/e4dratkp7xWc51TyBJocin2clLyWNX4S4AXu40rrPSO91L82gzFskSLEMBQlkxo5cniuzC5oeCvxKO2NG2ESZmtB8EuloOBbJhe9flUJDh0Nia+HBPhZIVM3p0/sUORcXzYOXB0C8D2SPa34IZ45apB6+5iv2QrqwHGwvSqc5Lw+31GzorNupyc+qa8tncv0aCjsIRO9F/gvG5cW2NMQEJRyyB/sipgpKdIPuNHwmMBCGAIDa6QLLNyr0BxbNDBixPfPac0t93a9Xa2RCCOcoTRb9NfX49OY6wuW+b36X8vPwSeAzaYO8ahqtvKI+3vC/466q8NDYXZuD1y7zSYtxN4aFGJRP7cCCjvha4t1ypIjjWe4xZrzG/l8Ozw5waOAXAg5UPhv4hdz0U1imZaCML5dxQlgjKbNhHtyvls8+eM24FwSvNA/hlqC3OpCw7VWtz4sK6zHVszt6MT/aZpj9TSxKOWAJ9slVRtqTpaW3tiDVuF1x9XXm8+frBnQ/XKZup5y/0IcvGKk/8dYPiiL1nR6nGlsSX+ysUtdWzg2cZcBlzQ39TWaKgdDc4y8E9rThjRuTir5VNbfGOm2YzPVgXKgc2IoOTc5y6pvTvPVS3dhyxTdmbKE0vZc43x9s9b2ttzqp4+drL+GU/2/O2U5Jecv0/Gr08cXoQrh1RHoc5YgDv2q78rW9eGmJi1piZ6OcuKE7kg2ugFDgcMwQd9ULCEUugT0S0+to75lCMWoy218A3D1nroKD2un+6a3M+DruF7rFZmnojm9CuBy4PTNHcN4ndKgWdybVCYVoh9fn1gDHVE8fmxjg9cBpQet6uw2IJBUgafxlny1RCBHbqPnjTPz0UKE9849IQM27j9ay82NSPLEN9eRbFzhStzYkbkiRxcJ1SzvdCuzGzRguWJQqE89L2KsxPxdEqlWh5DpCVKpes64XQNhdlsLEgHbfPb8i5maMuN29fHgbg3TtKlnm2uRCO2NG+o0y6jbM/0SMJRyyBPsleB6V7zF+eGCxLfARuGHidmmTjnoAU7guNxind+E2gLPGuTflkp5lbPnshjBwpfKXzFfyyn5qcGkrTF8hmbvuQ8vPSi8bbFE72QUcgYyIcyjBqizMoz0lh3uvnjVbj9aY+f0HJ5D2yhsoSBY9UK9UEb3S9wZTbWN/LoZmhYPBDOJTXUVALuRvA51auO6OxQFliOO8KlCf+9rzxxtU8f6EPr19ma0km6/PTtTYnrlRnVbPeuR6v38sbXW9obY6hSThiCfSL2csT/b6QUEdYf1g4osTohQv9hihPlGU5VJZYv7bKEgXCETs9cJrh2WGNrVkZv72qNNU/UvXIwk8o2q5sCr1z0GIw0Y6LvwZkRS0xq+Kmf5YkKZgVe9Zgm8KR6XmOtyvy7WtBtv5GNmdvpspZhdvvDqoPGoXD1w4jI7MjfwfF6Qt8dpIUVp5oMCXh2TFoD2zSxd9wA6JP7K3Lw4YTyhFBx/fWr61smOCBSuMGHfVEwhFLoF+2vk/5ee2IEs02G92nYGYYkp2w7sCCT7m3poBkm4Wrwy4u9EzE2cCV09A1TufoDKlJVu5fI2qJN1KcXsy2vG3IyIbaFPZO93J64DQSEg9XLRwYQJJCZX1GK09s+pXyc4GyRMF7A8GDl5sHDdWzcviiIhiwtSST8py1IRgQjiRJweDB8+3GChCIssTrRDpuZEvAiTFaJrrpGfB7oWAL5G1c8CkbCjKoKVKEcl400PiI3vFZTnYoaomPbl+jjlgg6PhOzzu4PC6NrTEuCUcsgX5xlkH5rYAciGabDCEDvvEBsC48BDLDYQ9Ka//idHe8LFs1IkL4wJZCUpPMP8R5McQCdajjkMaWRI7YwN5SdAtFaUuUtwlHpu0V40hqj3dB13FAuk4t8Ua2lmSyqTAdt9fPCxeME/x5+mwPAI9sW3vZMIFwxI73HTdMJnrANcDZwbNA6J6xIMX1kLsRvLNw0UCl+ud+ovzc8dElnyayYr810DX32/O9yDLcUplDSdba6ckMZ2PWRiozKw2ZidYTCUcsgb4xc3niArL1C/Gh3WWAokQ47/XF2qpV4/PLQRnitVqqIXio8iEkJE72n6R3Wv9lbrIs82ybUvb0rup3Lf3k/M1QuE2JdBulVErcP9YdgMzFnRVJknj/TuV6+1XAudE7XaMzHG8fRZLg/TvXZjkwQEVmBXW5dfhkHy91GEPVU5Ql7izYuXTwQ5JCzsy5H8fHuGgZvQpdx0CywLaPLPlUoZ74zpVhRl3ueFgXNSLo+O41vNZJkhTsz/xNm4ECBDoj4Ygl0Ddb3gtIyg19Qv8ZoYgZaYPhVrDYYMP9Sz71tg15FGU6mJj18ErzYJwMXDlH20YYnp4nO9XOHRvztTZHU4rTi7ml+BYAft2m/2xuy2gLVyeukmRJ4v51S38fAaj7gPLTKOWJwbLEDyz71PftLEGS4ET7KF2jMzE2LHp+eUa5L962Pm/NRuYFoqTWKOqJL3Qodi5ZlijY/hggQcdbMN4ZW8PU4PzPlJ/Vdy8Z/ABljt/Wkkx8ftkQ4yOuDk3T2DOJ1SLxyBpTBr6R96xXymaP9x2nb9o4GU09kXDEEuibzJJQ/9SFX2hri5qIbNi62yAla8mnWi0SH9ilRLr1XJ74q8CG8OFtxditiVvLe9e/F4BfX/k1ftmvsTVLI0Q67i6/m4ykjOVfIByajrf0P15ipA16zyqR+dr3Lvv0YmcK+6tzAfh1g76zYrIs86szio0f3L12s2GCh6qUTHTDUAM90/r+7NrG2zg/dB6rZI3MEcsqh6o7lMfnfxpb46JFlkOZux0fi+glQv5dBBb0jMiG3b4hj9z0ZI2t0ZbS9FL2Fu1FRubZqwapkNAZid1SAv2z/THl57kfKzd4M9CibHyXK0sUfDBQnvjGpSEGJ+diZdWqGXO5g/X9H9lTvsyz1wb3r7ufNHsaPdM9nBk4o7U5i+Lz+4L9YY9WPxrZi7IrQ+MlGnUeIDnzH8rP9fdCemSZ2vcFSvyePtuja7XSkx1jdI7OkJZkDfaSrmUKUgvYW7QX0H9W7FeXlSztnWV3kpeSF9mLhFPToPO1sOs4jHWAPQ1qlil1DvCBXaVYLRKnr41xeUC/giSyLAcdsfessdlhixEedNTz/VKvJByxBPpn6/vAmgxDLdB3TmtromeiBzqPKo8XkfS9kfX56eyqyMLnl3lGh1H6X57pxu31U1eayY4yp9bm6IIUWwoPVT4EwDNXntHWmCU43q+IGziTndxeenvkL6z/uPLz9Pf1uyn0eaDhP5XHuz4V8cserisi2WahbUjfaqW/DGTI37W9eE2L44QjRDuebXtWt5tCj88T7Mn8wMbly2WD1L4b7Kkw2qao7uoVkQ3b8l5ISovoJQUZDu6tUZR2f3qyK1aWRc2ZzjGuDrlw2C0c3FqotTm64IF1D5BiS6FzqpOGoQatzTEcCUcsgf5xOENRNaHCZGSEcEDFAXBGXk70od1KpukXp7t1tcHw+2V+dFzpWfjEreuQbhhMvZZ57wYlUvjStZeY8eiz3+i5q88BisCIfRH1zgXZ9mFlUzh8KRRY0ButL4BrENIKIs4+g6JWejCQYRKlf3pj1u3juUAW+oO7yjS2Rj8crDxIii2FqxNXOTOoz0z0692vMzY/Rn5K/sqCH8kZoeCdXkU7PHPQGFjjllFLvJGP7lXWuF+d7cHt1Wc5t1jrHt1eQoZjBfdLE5NqTw0OI//1Ff33ROuNhCOWwBiIkowLP1ei3EZGCBxEIBwQzru2F5Nss3BpYFpXUfojbSO0D7tIT7YlSjVuoD6/noqMCma9s7ocejkxPxFUmIu4LFHgyAxJ2Z/+vrqGqcWZHyg/6z++6IiIxfhAoDzx2XO9utwUvtjUz/S8l/KcFPZW5mhtjm7ISMoIZsV+1vozja1ZGFGW+N4N78VmWWEmUzg3jb8E77zKlqnApUMwPwGZZVB5x4peetemfAozkxl1uXm5WX+9p+Mzbp47rwQ/PnHrzUPh1zIi6Hio4xCz3lmNrTEWCUcsgTFYfy+k5SsDkK+8orU1q2f0KvSeUYQDtiwvHBCOMyU0U+w/j+tHNetHx68BSo1/WnKiPCocSZKCC5QeyxOfufIMc745NmVvYkf+jpUfYM/vKj+bntHfTLHxztC9YtcnV/zyOzbmUZCRzIjLzQuN+lMDE6IGH9xVhsWSyEKH8+HNHwYUefjROX19L/td/bzT8w4A79/w/pUfoOpOyCiBufHQLEo9cfaHys/tHwbLyraYNquFDwcqP36iw/LEX57pYd7rp7Y4k/ryLK3N0RW7C3dTml6Ky+PilU4D79E0IOGIJTAGVltoFoleSzIiQZQlVt0J6QUrfvnv7FsHKKUbI9PaR0MHJud46aISufzEres0tkafvGf9e5CQODVwiq4p/Wwu/LKfn7Yq6msfq/nY6kpKS3ZB0TbwzeuvbPjsU4CsROVz16/45TarhY8Hot4/ONKhrm1R0jU6w9tXlKHFibLEm9mau5WtuVvx+D26C4A8c+UZZGT2FO6hInMVWRWLNdSfeeJJdY2LluErcOVlQIKdj6/qEELs6a3LQ3SP6aecW5Zl/jMQdPzErRWJEvwbsEiWoGjHM5ef0dYYg5FwxBIYB1GS0foCzI5pa8tqaRTzjD64qpfvrcxmR5kTt9fPD49dU9Gw1fHTk134/DJ7K7PZXBSB7PkapCitiAMlygiGn7Tox1l5p+cduqa6yLCHSrlWjCTB7k8rj/Uk2uH3BRwxQvatgo/fWoHdKnGmc5wL3fopB/7+kQ5kWcnaleekam2OLnlss6K2+/PWn+tmfIRf9gcdwxWJdNzInt8DyQrX3oa+8+oYpwYn/q/yc9ODqwp+AFTkpnLbhlxkGX5+Sj9S9ifaR2kbcpGaZOW9a3iI81K8Z8N7sEgWjvcf5/LYZa3NMQwJRyyBcSjaBgVbleh70zNaW7NyhlphoFEZ4lyzwn6cAJIk8Zk7qgH44dFrzHl8alq4Irw+Pz8+ERLpSLA4n6j9BAC/vPxLpt3TGluj8JNWxSl838b3kWqPYjMfFO1o1Y9ox5WXYbIHUrJXfa2BouT2yDZlGO33dZIVm5rzBFXlPnN7lcbW6JcHKx8kw55B93Q3x3qPaW0OAMd6j9Ez3UO6PT2ywemL4SwNlbYf/z/qGBctcxPQ8CPl8a2/H9WhHturZAp/fkoJ9OkBIdLx3vqESMdilKaXcl/FfQA81fyUxtYYh4QjlsA4SFIoKyYkqY2EyIatvw9SV99c/0hdEaVZKYy43DxzVjtFt2fP99I3MUdOWhIP1SVmGC3FbaW3Ue2sxuVxBRv1taR7upu3ut8CQpmDVeNw6k+049R3lZ87PgZ2R1SH+tSBSkD5vuuhHPjnp7qZnveyPj+NOzdGNhdtLZJqT+Xd698NwM8u6UO04/tN3weUcuUUW0p0B9v3X5WfF34G00PRHUsNGv4T3NOQtxmq74nqUAe3FJKdaqd3Yo5Djf0qGbh6Rqbng3Z8/JZE0HEpHt+ilKT+tu23jMyOaGyNMUg4YgmMxfbHlIxS9wnob9TamsiR5TC1xNWVJQpsVgufDmwOn3y7XRMpe59f5l9fvQIoUXmH3Rp3G4yERbIEF6gfNf8Ir9+rqT0/v/xzZGRuK72NdZkqbCx2C9GOp2GyN/rjRcPARUW5DUkp4YqSneVZbA+UA2stIODzy3zvSDsAv3d7VUKkYxk+vEkR7Xi963UGXNqq8DWPNHO07yhWycont65cPOYmyvYqPZo+N5z+XvTHiwa/P5SZu/X3laBpFDjsVj65vxKAb7/Rpvm4lp+f7sbt87O9zMm2xJzMJanPr2db3jbcfrduAiB6J+GIJTAWGYWhUqNT39HWlpXQdw5GLiuDqVcwz2gxHrulnPRkG1cGp3n9Uvyjob8938vVIRdZqfZgxiDB0jxa/SjZydn0uno1VZVyy25+3abMevnY5o+pc9DSXVCxX9kUHvlf6hxztbzzDeVn7bshb2PUh5MkiU8FNoVPHbuG16ddv9HhiwN0jc6SlWrnAzsTIh3LsSF7A7sLd+OTfZqXSn2vUXGWHqx8kNL0yOdHLookhbJiJ58Erzv6Y66Wyy/BWLuSHV/h7LDF+NSBSlLsVi70TPDOFe0yK3MeH0++pQQ/Ht+XyIYthyRJwaDjT1p+wrxP+yoCvZNwxBIYj72fUX6e/xnMT2lrS6QISd+aR5T5S1GS6bDzWGD45ZNvXY36eCvhumzYbVWkJyTrI8Jhc/BYjVIG+B8X/0MzOxrcDUy6JylNL13ZMNmlkCS440vK49PfA5dGG6exDrjwC+XxHV9U7bCP7igmNy2JvomQSqgWfPcdZUP48VsqSElKZKEj4TN1ynrx09afaiZl3z3VzYvXXgTgd+t+V70Db3kfpBfB9ABcfEa9466U499Wfu76JCSlqXLInLSk4Br37TfaVDnmavjJiU6Gp+cpzUrhfTtVcKDXAPevu5+itCJG50Z5/qoORyzojIQjlsB4VN4BeZuUevTzP9XamuVxuxSnEWDXp1Q77O/eVonVIvHOlRFOtMdvg/FCYx9XBqfJdNj41G2VcTuvGXhs82PYLXbOD52nYbAh7uef983z+tzrAHxyyyexWlTczG+4D4rrwTMDx76l3nFXwjvfBNmn9KiU7FTtsMk2a1DK/n+/dgW/BgICjT0TnGgfxWaRgmVbCZbn9tLb2Zq7lVnvLP/RpE0A5AdNP8Av+7mt5DZqcmrUO7AtKRSYPPYtbVRL+xvh6mvKbMy9T6h66M/eUYXVIvH2lWFNVEvnvT6+/YYS6Pyvd6/Hbk1smSPBbrHz8RplxMIPm3+oeWmp3kl8qxIYDyms9+Pkd/Ujmb0YTc/A/CRkrYOqu1Q7bFl2anDmyt8+dzEum0O/X+ZfXxG9YdVkJtSjVkReSh6PViultaJxP5786sqvmJQnKUot4kObPqTuwSUJ7vhT5fGJf1dU1OLJ1EBIsl7YoSK/G8j+NvVO8oIGAgL/9rqSFXjX9mKKnNEJkKwlJEniczs+B8CPW37M+Nx4XM8/OjcalKxXNRsm2P27YHNA79lAb2ScefV/KD+3vBey1S3dK8tO5b07FKl4LbJivzjdTf/kHIWZyXxod6IUeCV8cNMHSbGlcHnsMu/0vqO1Obom4YglMCY7Pga2FBhsgq7jWluzNGd+oPzc/SmwqHvJffGBTaQlWTnXPcGz52MvkvBiUz+tA1NkOGx8OpENWxWf3PJJJCRe6XyF80PxmwE0653lu02KmuBn6j5DkjVJ/ZPUPAr5NTA/oThj8eTYt5TRFmV7oVKlksswctKS+L2AXPw/H26Nq6z22c4xnrvQhyTB5+5a3XymtcxdZXdRk1PDjHeGHzb/MK7n/nHLj5nzzbEldwu3FN2i/gnS8+FWxdHk5b9WZujFi85jcOkFZabZPf89Jqf4/cD3/fnGPtqHXTE5x0J4fP5g8OP371yfEKRaIZlJmcFg3zfPfFM3s/z0SMIRS2BMUrJgW0B98KSORTsGmxVHUbJC/SdUP3x+RjJ/cM8GAL7+QktM54rNun187YUWQMkOOFMS2bDVsCF7A+9Z/x4A/unUP8WtbOMnLT9hZG6EbEs276l+T2xOYrGEslHHvqWU5caD2fHQfeD2L0at2rYYn72jiqxUO21DLp6O0+gIWZaD190Hd5VRWxx9j+laQ5IkPrddcVb+s/k/mZiPT7Z2dG6UH11UZmv9bt3vIsXoe8ntX1CEMoaa41euL8uK4wew8xOqCOMsxOaiDO6tKUCW4X8FepPjwdNne+gemyUvPYmP3VIRt/OaiSe2PUG6PZ3m0Waeu/qc1uboloQjlsC47AnUxl98BlzDmpqyKKcD2bDND0NGbGZtfeb2KkqcDnon5vjO2+0xOQfAN165ROfoDMVOB//lzuqYnWct8Pmdn8dhdXBm8Ayvdr4a8/O5PC6+26hkw+5x3IPdEkMneusHILsSZkbgxP+N3XnCeePr4J6C/FrY9FDMTpPpsAczUt94+RJub+yjvK+2DHKifZRkm4UvPrAp5uczK/dU3MPG7I1Me6b5z+b4zKH817P/ypRnitqcWh6oeCB2J0rJVgIQAK/9HXjmYncuwZWXofOIogR815/H9FR/eK8SbPzlmW7Odo7F9FygZMO+9Zri9D1xR3VCGGeVZDuy+ey2zwLwzbPfZM4bh++lAUk4YgmMS+kupSHf59ZnVswzB+d/ojxWUaTjRhx2K/+/h5QG8G+9doWhKfXlYht7JoISvn/z3rqEUmKUFKUVBSV+/+XMv+Dxe2J6vqcuPsX4/DjrMtaxw74jpufCagttzN74/2A8xrO3BppCM4we/FvVy39v5FP7K8nPSKZ7bJafnuyM6bm8Pj9/H8iGffq2SkqyohwCvIaxSBb+y/b/Aij9mf2u2Pb5NY8088tLyuzIP7vlz9QVxlmIW38fMkpgoiv2o138/lA27JYnwBlbNcGdFdl8cJfSo/WV3zTFvB/6e++00zEyQ3aqnd9JSNZHxSdqP0FRWhH9rn7+syX2AZBXu17FJ8exPFcFEo5YAmOz//PKz+Pfjl8ZVKQ0PwuzY5BZpijKxZD37ChhR5kTl9vHXz/bpGq5m9fn58u/uoDPL/OubcXcv6VQtWOvZT6z7TPkOHK4NnmNn7f+PGbn6Zvu43tNygyjz237HFYpDtHd7Y8pc8U8M3AohtFyWYbnvqQoJda+O+bXGUBKkjUYof/mq1eYno/dcO5fnunm8uA0Wal2/uDuDTE7z1rh4LqD7MjfwYx3hq8d/1rMziPLMn9/4u+RkXm48mF2F+6O2bmC2FPg7sC19uY/xlYsp+lXMHABkjNjIoyzEH/28GbSk22c657gF2e6Y3ae7rEZ/uXwZQC+/EgtaYmgY1Q4bA7+cOcfAvDk+SdjKpbzyrVX+NJbX+LJ6Sfx+mN3X1abhCOWwNhseR9kV8HsaKgMUC+IqOSuxyHG0VCLReIr79mKzSLx2/N9/Oi4epH67x/p4ELPBJkOG3/1ni2qHXetk2ZP4w92/AEA/3bu35h0T6p+DlmW+asjf4XL46I+v54H1sWwPCociwXe9c9gsUHLb+HSi7E5z4WfK+VRthR4MHYb6xv56N4KKnJSGZqa52+fuxiTc0zPe/nnw5cA+Pw9GxI9mSpgkSz81f6/wibZeLXrVV65FpvB6i92vMiZwTM4rA6+uEe9eXbLUv8JZbTL7Ci8/vexOcfMKLz0l8rjA38EqTmxOc8NFGQ4+OP7lD60/+9QC5Nz6lcRyLLMX/26iVmPj1uqcvhwQilRFR6tfpTN2ZuZ8kzx7fPfjsk5RudG+eqxrwJQbavGZjGOA51wxBIYG6sNbvtj5fHR/wVet7b2CDqPQedRsNiVIZdxYGdFNn8WKFH86rMXVZm70tI/yT+9pGwG/9sjtRRkJGSz1eQDmz5AlbOK8flxvnb8a6oLd/zy8i852neUZGsyf3Pb32CR4njLL9wC+xRHk+e/BO4ZdY8/Nwkv/YXy+M4vQVa5usdfgiSbha9/cDsAPz7RxWutg6qf469+3cTA5DzlOSk8vj9RHqUWG7M3BmXk/+743zHtnlb1+LPeWf7p9D8BSta7KC02vcELYrXBg3+nPD72LWh7Tf1zPP8lmOqFnPWw/w/UP/4SfOpAJdX5afz/27vzuCir/Q/gn9lgQGEIQQFFcIVU3CAD9yVRyrQs1y5pJi5lZnlvWt3Sutfimtmv9WbmVpqZW5p6NVTcxRVySREVFRNQVBgUhYE5vz8epZAZNud5hsHP+/WaV8PMec5859vpab7Pck7WjQJ8ujnF5v1vOp6BLScvQ6dR4YOnW8k3ucoDRq1SFx+QWHpyKQ5lHrL5Z8xImIFrt6+hqaEpeuh72Lx/ObEQI8fXdjhQ2wcw/lF9FnjeOVv6Z9thgLufYh87uksj9G5RDwVFZrz0wyHk5FX9qOHF63kYMX8/bpmK0LFJneI1y8h2dGodpkdMh0alwbqz67DmzBqb9Z1+Ix2zDs4CALzS7hUEGgJt1neFdZsiXZqbfQHY+bFt+46fAdzIlH4QdnzFtn1XQESTOnjhzhIOU1YcQXae7Q4CrUn6AysPX4RaBXw8qC2ctZwswJbGtB6Dhm4NcfnWZXx6+FOb9j3zwExk3MyAXy0/jGw50qZ9V0iz3n9OZPXzeOkMlq0cXQEcWynNAjxwLuBUy3Z9V4CTVo3pT7YEACzacw6Hzttu4o7c2yZMW3scgLRERNO6bjbrm4COfh3Rv0l/mIUZU3ZMseklihvPbcSv53+FVqXFexHvQatynLNhAAsxqgm0zkDHO/eK7f4/ZddRsSTjGJCyCVCpgU6TFP1olUqFWYPawN/TBWnXbmHy8t9QWFT5md2u3SzA8/P3I9OYj+b1auO/z4VCrebRQTm0r9ceL7WVjix/sO8DnM0+e9993ntJ4t8e/tt991klzrWBqDuXSO36xHZH6I8sl+4LBYComdI+wA6m9A1GY+9auJybX/wj7n6lXcvDP1cfAwBM6NkMHRopc+nXg0Sv1eOdCOnyumXJy3Ag44BN+l15aiVWnFoBFVR4N+Jd6LV2uoIg8t/SJYq56cAvr0r3Ut6vnIvAujuXWXZ7A2igwH1vFnRt7o0n2/ih0CwwfvEhZBrvfyY+IQTe++V3ZBrzEVjHFS/34P2Ycnj70bcR4B6AzLxMvLPnHZtcAZJ1KwszEmYAAGJax+Bhz4fvu0+lsRCjmiF0JKD3AK6elibJsKddn0j/bDEAqKP84qsGFx2+Gh4KJ40am09kYtSig8i5VfEzY3kFhRi18ADOXrkJP4Mei0Z1gMGV96fI6cVWLyLcNxy3Cm9h8vbJ9z3N77dHvy1xSaLsM7aVJbifNHmHKAJ+GgFcSb6//i4eAta8LD3vNAlo9th9h1hVep0Gswe3hVoFrEm6hHVH0u+rP1ORGRN/TERufiHCAh7CxJ78QSiXcN9wDGgyAAICr8a/ijPZZ+6rv6NXjmLGPukH4YR2E9CpfidbhFk1Tq7SGSu1DjixFkhacn/9mc3S2bX8HKB+qGITdFgTOzAEQfXccDk3H+MWH0J+4f0dfP1i62msOCSdgf7g6RAu3iwTV50rPur6EXRqHbalbcPSk0vvqz+T2YR3d7+L7PxsBHsGIyYkxjaBKoyFGNUMzm7S9L0AsHOW9D8Oe7h2VppRCgA6v2afGACENDDgi+Ht4KLTYMepKxj41W6cyyp/Vsnjl3IwZE4CktKy4eGqw3cvdoCvgVNmy02j1uDDLh/CU++J09mn8a+Ef6Goimd2v//9e3yW+BkAYHLYZPtckvhXKhXw5GeAf7j0Q27JoKqv+2e8BPw4HCjKl9YL6/WubWOtgrb+HsUzGr6x6hiOXa/amWOzWeBf635H4oVsuOm1+L+hbaHV8H/Rcvpn+D/R1rstcgtyMW7zOGTezKxSP1dvXcVr216DyWxCT/+exWsn2ZVfW6Dn29LzDf+o+tlocxHwyytA6g5A5wo8/Q2gse+BuVrOWnzzfCjc9VokXsjGuz9XfabglYcu4uM7k+K8P6AVOjb1smWodI+H6zyMyWFSIT/r4CwkXU6qUj+F5kK8tfMt7PxjJ3RqHf7d6d/Q2XlcVhX38lRzPDpOmk434yjw2/0daamy3Z8Bwgw0fQzwlXm9pnJEtvTB8nER8DXocebKTQz4cjeW7r9g8b6xvIJCfLDhBPp/sRtH/8iBm16LeSMe4XXyCvJy8UJsl1iooMLaM2vxjx3/qPSZsZ+Sf8LMAzMBAOPbjMew4GFyhFp5Oj0wdIm00HP2eamYquyis/m50nY3MqSFmwfOlX020oqa9FgzPNHaF6YigfnJamxNvlKp7U1FZvx9xW/4bu95AEDswNZo8JCrHKHSX+i1enze83MEugci42YGxm8Zj9yC3Er1cfXWVUyMn4jMvEwEugdiRucZyk6KU5aOE4FmkdIyEj8MBpL/V7ntiwqB1eOAxMXSpfb9Pwe8qsdZ2oA6tfD58PZQq4BlB9Pw1bYzlS7Gdp/OwpSVRwBI94VxzTBlDA8ejh7+PWAymzAmbgx2XNxRqe2LzEV4Z/c72HhuI7RqLT7p/gmCPINkilZ+1WRvQWQDrp5A139Iz7e8D+TbdjascuVmAEl3FizsrOCUxWVoVd+ANS93Qlt/D+TcMuHNVUcRNiMOoxcdxJztZ/DummN4fv5+dJ0Zj292nJXWCmvtiy2vd0NowEP2Dv+BE+EXgf90/Q90ah3izsdhTNyYCt3ULITAilMr8K+EfwEARrUahfFtxsscbSXV8gKGLwf0BiBtH7AgCsg6XbFt038D5nQDLiUCLp7AsKWA3l3eeCtBq1Hj0yFt8XireigSKkxYmoQtJyp2duVWQRHGfn8Iqw7/AY1ahY8HtcETrX1ljpju8tB74OveX8PLxQsp11MwbvM4XDBWbPmPo1eOYsi6IThy5Qhq62rj0x6forZTbZkjrgS1BhiyWLo8uKgAWPY3acKNiigqAFaOAo7+JC1D8ex8IORZeeOtpG7NvfHGnZmCP9qUjAlLEyu0rp8QAisPXcTY7w+h0CzwZBs/vNHHcX/IOxqVSoXYLrHoVL8TbhXewsStE7E6ZXWFti0yF2HanmlYd3YdtCotZnWbhW7+3WSOWF4sxKhmeXSstK7YjQxp4g4lbXlfumTK/1EgoKOyn12Guu56/DgmHFP6BiOonhtMRQKbT2Tiw/+dxHd7z2PHqSvIulGA+h4uWDDyEXw5vD3qunOaenuJahSFOb3nwE3nhsTLiYj+XzR2/bELZmH5ctvjV49j1KZReG/vewCA5x5+DpPaT6qeUy97NweG/iAVY5cOA3O6AAcXWJ9MQAhg3zfAt48B185IMzA+twLwbKRs3BWg1ajx8bMhaFfHDFORwJjvD2HqyiO4lH3LYnshBA6eu4bh3yZg68nL0OvUmPt8KJ7h2kWKq1+7Pr7q9RVq62rjyJUjeGbtM1h0fJHVy4OFEFiVsgojNo4oPhO25PElaOzRWOHIK0DrDAxaJN2naS4EVo4GNr0NGK3czygEvHKPQ/N9f+D3NYDGCRj8PdDyaWXjrqCxXRvj3X4toFWrsP5IOgZ8sQspmdbPamYab2P0ooOYvPw33MgvRMcmdTBrUGtORqUwV50rPu/5Ofo36Y8iUYR397yL2Qdn49pty7N8CiGw649dGLRuENacWQONSoPYrrHo1bCXwpHbnkrYeuGaB5DRaITBYEBOTg7c3e17lNZkMmHDhg14/PHHodM55vWy9+3EL9KRP60emHDQpusLWc1v2n5g3p3FckdvARqE2ewzbS05Ixe//HYJ567ehL+nKwLruCKgTi209few+03KHL9/On39NMZvGY+MmxkApB+LzzZ/Fq29WiM7PxvZ+dk4lHkIG1I3AACcNc54MeRFjGs9zmoRVm3ym3NRuvk/9c4lKQ07Ak17Ag06AD4hQOYx6b3Tm6WzYAAQ9Dgw4EvFFpCtCpPJhF/Wb8DO/IZYnXgJgDTldnR4ALo294arkwauThqkZN7A/N2pOHJnrT93vRbzRz6CsMDq+92qA7nHb1puGt7b+x72pe8DALSs0xK9A3qjqUdTNPFogiu3rmDrha3YcmEL0nLTAAA9/Hvgg84fVK8zYZaYzcCGycDB+dLfGiegzTDpodFJ94LdyIR5z+dQX9wvtdG6SGfU7DghTkUdOn8NLy05jExjPnQaFcIb10GPoLroHuSNgiIzfr9kxPFLRiw/mAbj7UI4adR49bFmGNu1saL3YlabfXA1IYTAZ4mf4duj3wKQlnTpG9gXzzR/BrV0tZBbkIvrt6/jp1M/Ff936aZzw7SO09AnsE+p/qpTfitaG7AQswEWYtWMEMDCfsD5XUCrZ4Fn59msa4v5NRcBc3sC6UlA278BT31ps8970HD8lpR1KwvfHv0Wa8+sLfPelX6N+2Fiu4nwrV32JW3VKr9ms7To7Jb3pMugrFHrpOm4Hx0rTfxRjf01v0cu5WLmxmTsS7W+jpOTVo2B7epjfPcmCKij7JpMjkiJ8SuEwOrTqzHrwCzkmqz/N+ekdsLYNmMxOmR09bknrDxCAClxwK7ZwIW9VpsVqXRA2AvQdJ4EGOorF999yrqRj9eWJWFnStmTAYXUN2DWoDYI8lH+HuhqtQ+uRjad24SFxxbi2NVjVtvo1DoMCx6GmJAYeOg9LLapTvmtaG3gWKueEVWESgX0/UC6p+TYCuCR0UBAhHyfd/g7qQhzNgCPTZPvc+iB4+XihakdpuLV9q/i13O/YvXp1bh66yo89Z7wcPaAl4sXBjYbiJZeLe0dauWp1dL6f0FRQMqv0lnliweAnDSgdj2gUTegcTegSU9FF0W3ldAAT/w4Jhw7U7Iwf3cqMo35yCsoRF5BEVydNBgU2gDDOjREndr2WQONLFOpVBjYbCA61++MNafXIOV6Ck7nnEZqTipcNC7o6t8VPf17onP9znDVOdiEKioV0DxSepzfC+z5TJrcSqWWHhonFDXpibibD6NX5HBoHKxQ8KrtjO9GdcCZKzcRf/Iytp68jAPnrkGv06CFrzta+LmjXUMPPBHiyxlJq5k+gX3QJ7APjmUdw9KTS7H94nbo1Dq4ObnBzckNzTyaIaZ1DOrXdpwDAxXFQoxqJt82QPtoqUhaPQYYuxNw8bD95+Rdk+4NA4AebwK169r+M+iB56J1wYCmAzCg6QB7h2J7dZoAdcYD4XcmF7ltlJajqOZnvypCpVKha3NvdG3ube9QqJLqutZFTOs/1yUqNBdCrVI7ztmv8gREWDxAaTaZkL9hgx0Csg2VSoWmdWujad3aiOnaGAWFZmjVKt4D5iBaebXCjM4z7B2GomrIHoXIgsh/Ax4BQPYFYN1r1icEuB9b3gduXQPqtgAecczFBImqFb17jSjCqGbRqrU1pwh7gDhp1SzCqFrjXoVqLr1BmnJXrZUWWU5cbNv+j/wEHFogPY+aCWh4gpmIiIiIKoaFGNVsDcKAnv+Unv/vDeDKKdv0m/4bsPYV6XmXvwONutimXyIiIiJ6ILAQo5qv46tA4+6AKQ/46XngZtkzKpXHyWSEdsUIoPA20CwS6PGWbeIkIiIiogeGwxRi169fR3R0NAwGAwwGA6Kjo5GdnV3mNiqVyuLjo48+Km7TvXv3Uu8PHTpU5m9DilKrgafnSDOxXTkBLHwCyM2sWl9FJoSd+xIq40XAswkwcC6gtu/aW0RERETkeBymEBs+fDiSkpKwceNGbNy4EUlJSYiOji5zm/T09BKP+fPnQ6VS4ZlnninRLiYmpkS7OXPmyPlVyB7cfICR6wE3P+DKSWDh40DOH5Xr42YWNEsHwfvGCQinWsDQH+SZiZGIiIiIajyHmF3gxIkT2LhxIxISEvDoo48CAObOnYuIiAgkJycjKCjI4nY+Pj4l/l6zZg169OiBxo0bl3jd1dW1VFuqgbyaAS9sABb1B66eBhZEAUO+l6a6L8+lRGBZNNQ5aShU64GB86GtGyx/zERERERUIzlEIbZ3714YDIbiIgwAwsPDYTAYsGfPHquF2F9lZmZi/fr1WLRoUan3lixZgsWLF6NevXqIiorCtGnT4OZmfcX1/Px85OfnF/9tNBoBSCt6m0ymynw1m7v7+faOo9pyawBEr4V2ydNQXU8F5nSFudWzKOr2FuDRsHT7vKtQH10GdfwMqIryYX6oMXbUi0F4w64QzLHNcfzKi/mVF/MrL+ZXfsyxvJhfeVWn/FY0BpUQciyuZFsffPABFi5ciFOnSs5417x5c7zwwgt48803y+1j5syZiI2NxaVLl6DX64tfnzt3Lho1agQfHx8cO3YMb775Jpo2bYq4uDirfU2fPh3vvfdeqdd/+OEHuLq6VuKbkb04m7LR8o+l8L++FwBQpNIi3RCKW05euK3zQJFaB9+cw/A2HocaRQCADPe2OBQwFoXaWvYMnYiIiIiqsby8PAwfPhw5OTlwd3e32s6uZ8SsFTR/deDAAQDSxBv3EkJYfN2S+fPn47nnnitRhAHS/WF3tWrVCs2aNUNYWBgOHz6M9u3bW+zrzTffxOuvv178t9FohL+/PyIjI8tMthJMJhPi4uLQu3dv6HQ6u8ZS/Q2HKf03aOLfhyZ1Oxpk77PYStQLgbnt31An9AX0KCxifmXE8Ssv5ldezK+8mF/5McfyYn7lVZ3ye/dqufLYtRCbMGFCuTMUBgYG4siRI8jMLD3L3ZUrV1CvXr1yP2fnzp1ITk7GsmXLym3bvn176HQ6pKSkWC3EnJ2d4ezsXOp1nU5n93/xd1WnWKq1hmHAiLXAud3AxQPAjUwgNx24dR3wDwdCnoXKqxk0ADQAoJJONTO/8mJ+5cX8yov5lRfzKz/mWF7Mr7yqQ34r+vl2LcS8vLzg5eVVbruIiAjk5ORg//796NChAwBg3759yMnJQceOHcvdft68eQgNDUWbNuVPynD8+HGYTCb4+vqW/wWo5gjsJD2IiIiIiBTgENPXP/zww+jbty9iYmKQkJCAhIQExMTEoF+/fiUm6ggODsbq1atLbGs0GrF8+XKMHj26VL9nzpzB+++/j4MHD+LcuXPYsGEDBg0ahHbt2qFTJ/4oJyIiIiIieThEIQZIMxuGhIQgMjISkZGRaN26Nb7//vsSbZKTk5GTk1PitR9//BFCCAwbNqxUn05OTtiyZQv69OmDoKAgTJw4EZGRkdi8eTM0Gi7SS0RERERE8nCI6esBwNPTE4sXLy6zjaUJIMeMGYMxY8ZYbO/v74/t27fbJD4iIiIiIqKKcpgzYkRERERERDUFCzEiIiIiIiKFsRAjIiIiIiJSGAsxIiIiIiIihbEQIyIiIiIiUhgLMSIiIiIiIoWxECMiIiIiIlIYCzEiIiIiIiKFsRAjIiIiIiJSGAsxIiIiIiIihbEQIyIiIiIiUhgLMSIiIiIiIoWxECMiIiIiIlIYCzEiIiIiIiKFsRAjIiIiIiJSmNbeAdQEQggAgNFotHMkgMlkQl5eHoxGI3Q6nb3DqXGYX3kxv/JifuXF/MqL+ZUfcywv5lde1Sm/d2uCuzWCNSzEbCA3NxcA4O/vb+dIiIiIiIioOsjNzYXBYLD6vkqUV6pRucxmMy5dugQ3NzeoVCq7xmI0GuHv74+0tDS4u7vbNZaaiPmVF/MrL+ZXXsyvvJhf+THH8mJ+5VWd8iuEQG5uLvz8/KBWW78TjGfEbECtVqNBgwb2DqMEd3d3uw/Cmoz5lRfzKy/mV17Mr7yYX/kxx/JifuVVXfJb1pmwuzhZBxERERERkcJYiBERERERESmMhVgN4+zsjGnTpsHZ2dneodRIzK+8mF95Mb/yYn7lxfzKjzmWF/MrL0fMLyfrICIiIiIiUhjPiBERERERESmMhRgREREREZHCWIgREREREREpjIUYERERERGRwliIOaCvvvoKjRo1gl6vR2hoKHbu3Flm++3btyM0NBR6vR6NGzfG119/rVCkjuXDDz/EI488Ajc3N9StWxdPPfUUkpOTy9xm27ZtUKlUpR4nT55UKGrHMX369FJ58vHxKXMbjt2KCwwMtDgWX375ZYvtOXbLtmPHDjz55JPw8/ODSqXCzz//XOJ9IQSmT58OPz8/uLi4oHv37jh+/Hi5/a5cuRItWrSAs7MzWrRogdWrV8v0Daq/snJsMpkwZcoUhISEoFatWvDz88Pzzz+PS5culdnnwoULLY7r27dvy/xtqp/yxvDIkSNL5Sk8PLzcfjmGJeXl19I4VKlU+Oijj6z2yfErqcjvsZqyD2Yh5mCWLVuGSZMm4e2330ZiYiK6dOmCqKgoXLhwwWL71NRUPP744+jSpQsSExPx1ltvYeLEiVi5cqXCkVd/27dvx8svv4yEhATExcWhsLAQkZGRuHnzZrnbJicnIz09vfjRrFkzBSJ2PC1btiyRp6NHj1pty7FbOQcOHCiR27i4OADAoEGDytyOY9eymzdvok2bNvjiiy8svj9z5kzMnj0bX3zxBQ4cOAAfHx/07t0bubm5Vvvcu3cvhgwZgujoaPz222+Ijo7G4MGDsW/fPrm+RrVWVo7z8vJw+PBhvPPOOzh8+DBWrVqFU6dOoX///uX26+7uXmJMp6enQ6/Xy/EVqrXyxjAA9O3bt0SeNmzYUGafHMN/Ki+/947B+fPnQ6VS4ZlnnimzX47fiv0eqzH7YEEOpUOHDmLcuHElXgsODhZTp0612P6NN94QwcHBJV4bO3asCA8Ply3GmuLy5csCgNi+fbvVNvHx8QKAuH79unKBOahp06aJNm3aVLg9x+79efXVV0WTJk2E2Wy2+D7HbsUBEKtXry7+22w2Cx8fHxEbG1v82u3bt4XBYBBff/211X4GDx4s+vbtW+K1Pn36iKFDh9o8Zkdzb44t2b9/vwAgzp8/b7XNggULhMFgsG1wNYCl/I4YMUIMGDCgUv1wDFtWkfE7YMAA0bNnzzLbcPxadu/vsZq0D+YZMQdSUFCAQ4cOITIyssTrkZGR2LNnj8Vt9u7dW6p9nz59cPDgQZhMJtlirQlycnIAAJ6enuW2bdeuHXx9fdGrVy/Ex8fLHZrDSklJgZ+fHxo1aoShQ4fi7NmzVtty7FZdQUEBFi9ejFGjRkGlUpXZlmO38lJTU5GRkVFifDo7O6Nbt25W98WA9TFd1jb0p5ycHKhUKnh4eJTZ7saNGwgICECDBg3Qr18/JCYmKhOgA9q2bRvq1q2L5s2bIyYmBpcvXy6zPcdw1WRmZmL9+vV48cUXy23L8Vvavb/HatI+mIWYA8nKykJRURHq1atX4vV69eohIyPD4jYZGRkW2xcWFiIrK0u2WB2dEAKvv/46OnfujFatWllt5+vri2+++QYrV67EqlWrEBQUhF69emHHjh0KRusYHn30UXz33XfYtGkT5s6di4yMDHTs2BFXr1612J5jt+p+/vlnZGdnY+TIkVbbcOxW3d39bWX2xXe3q+w2JLl9+zamTp2K4cOHw93d3Wq74OBgLFy4EGvXrsXSpUuh1+vRqVMnpKSkKBitY4iKisKSJUuwdetWfPzxxzhw4AB69uyJ/Px8q9twDFfNokWL4ObmhoEDB5bZjuO3NEu/x2rSPlhrt0+mKrv3CLcQosyj3pbaW3qd/jRhwgQcOXIEu3btKrNdUFAQgoKCiv+OiIhAWloaZs2aha5du8odpkOJiooqfh4SEoKIiAg0adIEixYtwuuvv25xG47dqpk3bx6ioqLg5+dntQ3H7v2r7L64qts86EwmE4YOHQqz2YyvvvqqzLbh4eElJpzo1KkT2rdvj88//xyfffaZ3KE6lCFDhhQ/b9WqFcLCwhAQEID169eXWTBwDFfe/Pnz8dxzz5V7rxfHb2ll/R6rCftgnhFzIF5eXtBoNKUq98uXL5eq8O/y8fGx2F6r1aJOnTqyxerIXnnlFaxduxbx8fFo0KBBpbcPDw9/oI9eVVStWrUQEhJiNVccu1Vz/vx5bN68GaNHj670thy7FXN3ts/K7IvvblfZbR50JpMJgwcPRmpqKuLi4so8G2aJWq3GI488wnFdAb6+vggICCgzVxzDlbdz504kJydXaZ/8oI9fa7/HatI+mIWYA3FyckJoaGjxbGh3xcXFoWPHjha3iYiIKNX+119/RVhYGHQ6nWyxOiIhBCZMmIBVq1Zh69ataNSoUZX6SUxMhK+vr42jq3ny8/Nx4sQJq7ni2K2aBQsWoG7dunjiiScqvS3HbsU0atQIPj4+JcZnQUEBtm/fbnVfDFgf02Vt8yC7W4SlpKRg8+bNVToAI4RAUlISx3UFXL16FWlpaWXmimO48ubNm4fQ0FC0adOm0ts+qOO3vN9jNWofbI8ZQqjqfvzxR6HT6cS8efPE77//LiZNmiRq1aolzp07J4QQYurUqSI6Orq4/dmzZ4Wrq6t47bXXxO+//y7mzZsndDqdWLFihb2+QrU1fvx4YTAYxLZt20R6enrxIy8vr7jNvfn95JNPxOrVq8WpU6fEsWPHxNSpUwUAsXLlSnt8hWpt8uTJYtu2beLs2bMiISFB9OvXT7i5uXHs2lBRUZFo2LChmDJlSqn3OHYrJzc3VyQmJorExEQBQMyePVskJiYWz9gXGxsrDAaDWLVqlTh69KgYNmyY8PX1FUajsbiP6OjoEjPa7t69W2g0GhEbGytOnDghYmNjhVarFQkJCYp/v+qgrBybTCbRv39/0aBBA5GUlFRin5yfn1/cx705nj59uti4caM4c+aMSExMFC+88ILQarVi37599viKdlVWfnNzc8XkyZPFnj17RGpqqoiPjxcRERGifv36HMMVVN4+QgghcnJyhKurq/jvf/9rsQ+OX8sq8nuspuyDWYg5oC+//FIEBAQIJycn0b59+xLTq48YMUJ069atRPtt27aJdu3aCScnJxEYGGh1h/CgA2DxsWDBguI29+b3P//5j2jSpInQ6/XioYceEp07dxbr169XPngHMGTIEOHr6yt0Op3w8/MTAwcOFMePHy9+n2P3/m3atEkAEMnJyaXe49itnLvT+9/7GDFihBBCmj552rRpwsfHRzg7O4uuXbuKo0ePluijW7duxe3vWr58uQgKChI6nU4EBwc/0IVvWTlOTU21uk+Oj48v7uPeHE+aNEk0bNhQODk5CW9vbxEZGSn27Nmj/JerBsrKb15enoiMjBTe3t5Cp9OJhg0bihEjRogLFy6U6INj2Lry9hFCCDFnzhzh4uIisrOzLfbB8WtZRX6P1ZR9sEqIO3e/ExERERERkSJ4jxgREREREZHCWIgREREREREpjIUYERERERGRwliIERERERERKYyFGBERERERkcJYiBERERERESmMhRgREREREZHCWIgREREREREpjIUYERERERGRwrT2DoCIiKg62rNnD1566SWL7/Xt2xexsbF47LHHkJWVZbHN/v374eTkVOr1kSNHIjs7Gz///LMtwyUiIgfDQoyIiMgCo9GIp556CtOnTy/x+rlz5zB16lQAwI0bN5CUlFRq2+7du8NsNisQJREROSoWYkRERAqZPn06Fi1aBABQqVQAgPj4eHTv3t2OURERkT2wECMiIlLI3//+d5w4cQJGoxELFiwAAHh6eto5KiIisgcWYkRERAqpXbs2XFxckJ+fDx8fH3uHQ0REdsRZE4mIiIiIiBTGQoyIiIiIiEhhLMSIiIgU5OTkhKKiInuHQUREdsZCjIiISEGBgYE4cuQIkpOTkZWVBZPJZO+QiIjIDliIERERKSgmJgZBQUEICwuDt7c3du/ebe+QiIjIDjhrIhERkYK8vb3x66+/2jsMIiKyM54RIyIiIiIiUhjPiBEREVlgMBiwbt06rFu3rtR7ffr0AQB4eHggLCzM4vZqNY91EhGRdSohhLB3EERERERERA8SHq4jIiIiIiJSGAsxIiIiIiIihbEQIyIiIiIiUhgLMSIiIiIiIoWxECMiIiIiIlIYCzEiIiIiIiKFsRAjIiIiIiJSGAsxIiIiIiIihf0/gqn/0h6V7rMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.integrate import odeint\n",
    "\n",
    "# 建立三阶震动方程\n",
    "def third_order_oscillation(y, t):\n",
    "    return [y[1], y[2], -y[0] - y[1] - y[2]]\n",
    "\n",
    "# 初始条件\n",
    "y0 = [1.0, 0.0, 0.0]\n",
    "\n",
    "# 时间范围\n",
    "t = np.linspace(0, 20, 200)\n",
    "\n",
    "# 求解微分方程\n",
    "sol = odeint(third_order_oscillation, y0, t)\n",
    "\n",
    "# 可视化分析\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(t, sol[:, 0], label='y(t)')\n",
    "plt.plot(t, sol[:, 1], label=\"y'(t)\")\n",
    "plt.plot(t, sol[:, 2], label=\"y''(t)\")\n",
    "plt.xlabel('时间 t')\n",
    "plt.ylabel('振幅')\n",
    "plt.title('三阶震动方程的可视化分析')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
